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Predicting Employee Satisfaction and Turnover Rates with Machine Learning

Every startup business faces one crucial initial challenge: Capturing and retaining good employees. Without that key element, success can be very elusive.
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Every startup business faces one crucial initial challenge: Capturing and retaining good employees. Without that key element, success can be very elusive.

The statistics for startups show that in various branches of business and industry, the numbers can be daunting:

For Finance, Insurance, and Real Estate, for instance, only 58 % of new companies are still in business after four years. In Retail, only 48% are still on their feet after four years.

And with Information startups, only 37% are still running after four years.

No wonder entrepreneurs sometimes feel as if they are playing a lottery when it comes to hiring new staff that is a consistent cut above merely competent; you pay your money, and you take your chances. To even the odds in your favor, consider integrating machine learning into your hiring process.

Machine learning is the term that HR departments, benefits managers, and employee relations experts use to explain how computers can be programmed to sort through massive amounts of data on resumes to highlight those individuals who most likely will fit a company's profile. It will help identify which candidate appears capable of a long-term employment commitment, and who will add real value to their employer's enterprise.

Anyone who has started a new business knows that spending long, tedious hours interviewing a broad range of applicants does not always bring in the best employees and is no guarantee of a satisfying and profitable, long-lasting relationship.

Wayne Fletcher, an entrepreneur consultant, explains: "Entrepreneurs talk about branding strategy and funneling traffic to specific sites as benchmarks for success. But if you don't have the people in place who know their job and do it with brilliance and enthusiasm, you're just spinning wheels without engaging the gears. It's like the Yankees this season; they're not going to the World Series this year because they've cut back on all their premier talent to concentrate on other areas like merchandising. For a deeply entrenched organization like the Yankees, this may make sense -- but for a startup business to downplay personnel is practically suicide."

An entrepreneur who relies on a 'gut feeling' when recruiting new staff is likely to wind up with nothing more than an ulcer in the long run.

With machine learning, a list is generated of candidates who meet the parameters and requirements necessary to succeed in a position with your company. Machine learning looks past education and previous experience to indicate desirable patterns, such as diverse work experience and outside recognition of significant achievements. You get the cream of the crop.

Then it is up to you and your HR and Benefits people to craft a hiring package that appeals to the candidate and meets his or her requirements -- both spoken and unspoken.

An excellent staff is not built by bargaining down a potential employee, seeing how little you have to offer to get them to sign on the dotted line. That is almost a sure guarantee of job dissatisfaction and an untimely and probably acrimonious exit. It's also counter-productive to make extravagant claims about your startup to tempt the strongest talent; once you've lost their confidence by overstating the benefits, it's almost impossible to recover it.

According to entrepreneur Bennett Adamson: "For smaller startups, the talent pool of active candidates is pretty small and difficult to locate. Small businesses have to compete with large ones for the same people, and while large companies can often offer more extensive benefits packages, that isn't the whole deal. Smaller companies can sell themselves as a place where a new employee feels more needed and has a better chance for timely advancement. That can be a crucial bargaining chip for the new business."

Employee demographics also play a significant role in employee retention. For instance, says Adamson, if a majority of employees are Millennials then tailoring their benefits package accordingly can increase employee longevity quite a bit.

"A coffee bar instead of a break room not only gives Millennials all the caffeine they crave but the motivation to stay on forever" Adamson only half-jokes.

It's a logical two-step: First use machine learning to find the very best candidates out there, then be flexible enough to offer them a benefits program that appeals to their demographics and specific needs.

Bottom Line:

Machine learning, plus flexibility is how successful startups build a team that gets results stays put, and increases in expertise and tangible value.

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