Talent Analytics, Corp. has a unique approach to workforce predictive analytics. At our firm, we measure success by how our projects quantifiably benefit the line of business. We watch it, track it, and report success. Our algorithms get better and smarter using the best data science methods available. I’ve been involved in the predictive workforce arena for almost two decades. I have to admit I’m surprised at how many vendors claiming to reduce employee turnover or increase employee performance do little more than offer a solution that “sounds” effective. They say the right predictive analytics buzzwords – without proving that their solutions actually work for their customers.

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Inquiry Response: Scaling Analytics Talent Growth

By Greta Roberts, Feb 06, 2017

Available to Research & Advisory Network Clients Only

We are strategizing how to scale our analytics talent over the next few years, and looking for insights on how to determine how many resources are enough.

Questions:

  • What business drivers would cause our organization to grow our talent base? Do we have enough talent as it is?

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Over the past 30+ years, businesses have spent billions on talent assessments. Many of these are now being used to understand job candidates. Increasingly, businesses are asking how (or if) a predictive talent acquisition strategy can include the use of pre-hire assessments? As costs of failed new hires continue to rise, recruiters and hiring managers are looking for any kind of pre-hire information to increase the probability of making a great hire.

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For most companies, their current pre-hire talent assessments are wasted data. Results are delivered in an individual report that cannot be analyzed or aggregated. For most “legacy” talent assessments, it’s difficult or impossible to determine what positive (or negative) business effect the assessments are having. It often comes down to the question of “how much the HR person believes the results.” This is a bad measure of success. But it doesn’t have to be that way.

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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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