Our predictive workforce assignments yield staggering results; saving / making businesses millions of real, measurable dollars. Often this yield is in a single project. Business ROI with these predictive projects is so significant, I wanted to share some of our findings as they may challenge some concepts we hold so closely.
Large organizations spend millions on training, coaching, mentoring, re-training and competency development programs. We do this believing employee development programs can train someone to better or even great performance. It seems to make sense. And, the only option a business has after hiring someone is try to develop employees to greatness. Better training, better managers, better raises, better perks, better culture, better benefits, more time off, better work life balance, more and more and more that the organization needs to do, to prop up and hopefully “develop” the employee into being a better performer. But does it work?
Data Science Studies: Do Development Programs Help Increase Performance Over Time?
We recently completed two analytics studies quantitatively analyzing sales rep. performance. Among other goals, we analyzed sales rep. performance over time – both before and after training is completed. We documented their sales performance as new hires, during training, and finally after they reached full self-sufficiency in their role.
Results: Two Data Science Studies Sales Rep. Performance Does Not Measurably Increase Over Time — Even With Substantial Training and Development by the Business
What we found is perhaps shocking to many, though we see this time and again in our predictive analytics work across many roles. For the purposes of this paper, we’ll show graphs from the work we did studying Underwriter sales performance.
In both of sales projects referenced here, analytics results showed that sales rep performance did not measurably increase over time – despite multi-millions being spent on development efforts including: training, coaching, competency development and the like.
Top performers began as top performers – and continued to be top performers.
Business Cost, of Developing “Bottom Performers” With Hopes of Turning them Around
In many of today’s businesses around the world, when a bottom performer unfortunately enters as an employee, massive support systems are engaged to prop up, support, train, coach, prompt, and cajole that bad hire into some kind of average performance.
The support systems required are extraordinary — and very, very, very expensive. The best possible outcome is that you can nurture them to averageness, not to greatness.
The only thing your organization can do once hired, is to try to develop that low or average performer, hoping to squeeze some kind of value out of them. It’s all you can do once they are hired.
The greatest cost to the business, seen in Figure 2, is what could literally lead a company to either mediocrity or wild success.
This difference is worth mega $millions (purposely not revealing too much so as to protect the identity of our client).
Options? Predict Top and Bottom Performers – Before You Hire Them
The financially optimal solution is to predict and screen in top performers and screen out bottom performers before they enter as an employee. Today there are people in your organization that are performing very, very well without need of an expensive and extensive support organization. Yes, they need some managing and coaching here and there. They needed time to ramp up to full productivity, but guidance they need now is minimal. They don’t need propping up. You are not their crutch.
You and your teams can tell that it’s in their nature to excel in this role. Despite your competitor being able to pay more, despite not having a manager for a few months, despite not having training on their first day or a raise in more than a year, or time off, or or or or – – they continue to consistently outperform.
You need more of these employees and predictive models can find them – pre-hire.
Conversely, there are employees in this same role that require extensive coaching, training, mentoring, special perks and other types of support. Our data consistently shows that all this development will have little impact on their ability to perform on their own, without the extensive support network being provided to them.
Predicting top / bottom / average performance is a perfect situation to apply data science. A data science approach helps investigate potential differences in the nature of the successful and unsuccessful performers. Findings help predict the performance you’re looking for, pre-hire.
Nature and Nurture are Both Important. But Nature Comes First
Nurture is irrelevant – if the nature of your employees continue to fight your nurturing. (People don’t want to be changed). You can’t change the nature of your employees. Period. If you could, you could stop interviewing. You could hire anyone, for any job and train them to be top performers at anything.
Marketeers and Political Candidates would laugh at this concept.
You can’t fix an employee attrition or performance problem by simply increasing the size of your development / support / training / mentoring / managing departments. That simply increases the financial spend in this area.
The key is to start with predicting the nature that is optimized for your role – and develop from there. Every other human domain area uses this approach, except for the employee / job candidate domain.
Consumers are people. Voters are people. Employees are people too. Predicting behavior and optimizing performance is the same in all these domains. Begin by understanding the human’s nature, then align the offer (a coupon, a political candidate, or a job) with their nature and finally nurture – the right nature – to greatness.
Our work consistently shows that top and bottom performers in a specific role have different natures. Not only sales reps, but call center reps, bank tellers, financial advisors, truck drivers, insurance agents, engineers and the like. It’s not random that people excel in their role. They excel because it is their nature to excel in the role. They’re built for it, learn quickly, feel valued and satisfied.
They gobble up the training and quickly implement. They love that the nature they are is valued – just as it is.
We’re data scientists. We analyze data, see what we see and build predictive models when there is a strong prediction. Predictions are deployed quickly on our light touch, cloud solution Advisor(™) making it easy for recruiters and hiring managers to predict performance pre-hire. Using machine learning, our algorithms learn and get smarter over time (like the recommendation engine in Netflix or Amazon).
Leading organizations are using a predictive workforce analytics approach now, to solve these challenges. They are competing on talent analytics. It’s easier than you think. It’s more respectful of your employees. It’s less costly and stops the farce of thinking we are powerful enough to develop anyone, to be anything we need them to be (whether they like it or not).
Greta Roberts is the CEO & co-founder of Talent Analytics, Corp., Chair of Predictive Analytics World for Workforce and Faculty member of the International Institute for Analytics. Follow her on twitter @gretaroberts.