retention strategies

Big Data Knows You’re Going to Quit Your Job Before You Do

Are you happy on your job? Are you unsatisfied with your work? Perhaps, you’ve already begun spending quality time on in search of new opportunities? And yet, you’ve kept all this to yourself, as you continue to plod along in a job you are starting to hate. What if I told you that with the help of big data and magic algorithms, companies are able to spot employees like you and with that knowledge, offer you a promotion, a raise or more fulfilling tasks, all in an effort to retain you as their employee. Sound crazy? Its not. IBM has software that can predict which workers are about to quit their jobs with 95% accuracy. Tune in to hear a very interesting  podcast!

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About the host:

Over the past decade, Jim Stroud has built an expertise in sourcing and recruiting strategy, public speaking, lead generation, video production, podcasting, online research, competitive intelligence, online community management and training. He has consulted for such companies as Microsoft, Google, MCI, Siemens, Bernard Hodes Group and a host of startup companies. During his tenure with Randstad Sourceright, he alleviated the recruitment headaches of their clients worldwide as their Global Head of Sourcing and Recruiting Strategy.  He now serves ClickIQ as its VP, Product Evangelist.


When you think of prisoners working inside of a jail, what comes to mind? Prisoners cooking, mopping floors, folding clothes…? Yes, all of those are certainly true and now you can add one more – training artificial intelligence algorithms. I’ll explain, after this.

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IBM CEO Ginni Rometty spoke at CNBC’s @ Work Talent + HR Summit on the future of work; specifically, her talk was on AI and how it would change jobs. Here’s a clip.


From this interview and other places online, I discovered a few more interesting things that IBM is doing, as reported by CNBC.

  • [quote] IBM HR has a patent for its “predictive attrition program” which was developed with Watson to predict employee flight risk and prescribe actions for managers to engage employees. Rometty would not explain “the secret sauce” that allowed the AI to work so effectively in identifying workers about to jump (officially, IBM said the predictions are now in the 95 percent accuracy “range”). Rometty would only say that its success comes through analyzing many data points.
  • [quote] IBM’s MYCA (My Career Advisor) AI virtual assistant uses Watson to help employees identify where they need to increase their skills. Its companion, Blue Match technology, serves up job openings to employees based on their AI-inferred skills data (employees opt into the service). Rometty said some of the 27 percent of IBM workers who received a new job or promotion in 2018 were assisted by Blue Match.
  • [quote] IBM employees no longer need to decipher which programs will help them upskill; its AI suggests to each employee what they should be learning in order to get ahead in their career.

I see what IBM is doing today as a natural progression of things. Using big data, to resolve retention issues has been in the works for some time. A few years ago, in 2014, Workday acquired a startup called – Identified which was doing some remarkable work with its predictive algorithms. I don’t know to what extent Workday has integrated and leveraged Identified’s technology today but, I can tell you what it was capable of back then.

  • Mohammad Sabah was Identified’s head of data science. He was previously at Netflix where he worked on their movie recommendation algorithm. In 2014, Bloomberg quoted Sabah when he compared his Netflix work with Identified. Sabah said, “The domain is so different, but the techniques and the algorithms and the tools are general.”
  • That same Bloomberg article goes on to say, [quote] “By combining company data on employee hiring, promotions, relocations, compensation, employee satisfaction surveys, managerial decisions and job cuts with public data sets like the standard of living in the region and workforce demand for certain skills, Workday can spot patterns.”
  • And even deeper in that article it cites how businesses can input decades of historical staff data to inform and customize the system’s recommendations. The system learns over time how each company works and, like an experienced HR employee, develops a gut feeling for which people the company needs to keep a closer eye on.

If IBM represents the state of the art and Workday the preceding evolutionary step then, Google would have to be the mother of the movement of using big data to predict employee departures. As far back as 2009, Google had developed a workforce prediction algorithm which tracked employees who were about to jump ship. The Wall Street Journal reported on the tech back then and reported that Google examined data from employee reviews and promotion and pay histories to try to identify which of its 20,000 employees were most likely to leave the California-based company. Laszlo Bock, who runs human resources for Google, told the Journal the algorithm helps the company “get inside people’s heads even before they know they might leave.”

When I mentioned what IBM was doing today with their magic algorithms, I could almost see your surprise, now imagine the shock the HR world had in 2009. Just for giggles, let me share a few reactions from people discussing Google’s Workforce Prediction Algorithm back then and see if they still resonate today.

  • REAL predictive analytics finally gets a showing! So many people are using the term predictive analytics about things which are really just metrics and reporting…it’s a wonderful thing to see real PA at least being thought about.
  • Google searches are great, but they don’t get everything…and if management at Google starts to think that they do, there is a serious risk of complacency and so further loss of focus on the value of human management. 
  • If we predict individual human behavior, what risks do we open up? Lawsuits, even?  What if we get it wrong about Sally and don’t promote her because the algorithm said she’s likely to leave?  Sure, we already do that in management heads, but what’s the legal situation once it comes from an algorithm?
  • Predicting how Individuals will perform is already an accepted and proven fact today in the US. The US FICO score is a predicted score of an individual’s credit worthiness and is used in our everyday life. The facts show that people who defaulted on loans in this housing crash, were people that had a FICO score that should have prevented them from getting a loan in the first place. Talk about a self fulling prophecy.
  • For the workforce, your bosses boss or even your boss’s boss’s boss has final say on what raise you get, bonus, promotion, etc. Is their intuition good enough to make the right decisions for you as an individual they may only know from a few meetings or passing in the hall? Predictive Analytics in the workforce will be able to provide them with the facts and the impacts of the decision they are about to make.

I think using big data to inform our decisions is a good thing. However, combining human judgement with big data insights, is the greater thing. Machines are our helpers, they augment our abilities and have the capacity to transform us all into Tony Stark. If we relax our input and rely solely on the decision making capacities of a machine then, that’s when the terminators come. At least, I think so. What do you think? Leave a comment on my blog or wherever you are listening to this podcast. I want to know what you think.

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Links related to this podcast:

IBM AI can predict with 95 percent accuracy which employees will quit 

Workday Predicts When Employees Will Quit – Business Insider 

Google gets mathematical on staff ‘brain drain’ – ABC News (Australian Broadcasting Corporation) 

Google workforce prediction algorithm? | Strategic Workforce Planning 

(259) IBM’s Ginni Rometty: AI will change 100 percent of jobs – YouTube 


Music in this podcast:

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