Can Employee Development Lead to Business Mediocrity?

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Author: Greta Roberts | Source: HCI | Published: October 13, 2015

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?

Here’s the thing.

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.

  • Both sets of analyses were for sales role at vastly different types of companies
  • The first sales group consisted of financial advisors
  • The second sales group sold web / email and internet connectivity into consumer homes
  • Each business measured real sales performance (not performance ratings) over time, during and beyond formal and informal training.  
  • Both companies had made a significant, long term financial investment in formal training and coaching development programs, to help sales reps to optimize their sales performance

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.

  • Figure 1 shows one example of Insurance Underwriters. In this case, performance is measured by sales and bookings - so a lovely quantitative measure.  This is not performance review information, this is actual sales performance in the job.
  • Breaking out data by Performance Clusters, we begin to see the true stratification that exists inside this group.  Figure 1 shows the five levels, from the 5% percentile to the 95% percentile performers.
  • Important lessons are evident:
    • Relative Performance Levels are evident very early in tenure, even in the first quarters
    • Growth after initial training is only gradual (if at all) - with the exception of the top performers after quite some time
    • At the individual level, we do not see underwriters crossing categories or increasing after the first year.  Performance patterns are set in very early.
    • The first year, or even six months of performance appears to lay the baseline for the rest of an underwriter’s 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.

Figure 1


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.  

  • Note that the development and experience only made a significant impact on top performers sales and bookings over time.
  • Average performers began and continued to be average
  • Bottom performers began and continued to be bottom.

Training might have had a short term impact, or a small impact.  But long termbusiness performance was “consistent” from the beginning throughout their tenure - with the single exception of the top performers who excelled even more once nurturing was applied to the ideal nature for the role.

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.  

  • After 5 years, differing performance adds up significantly.  Figure 2 illustrates the many $Million spread between the trajectories of the top and bottom quantiles of underwriter performance. This is a hypothetical plot as we did not have 12 years worth of transaction data. The plot is based on the average book for each performance group, extended linearly to 12 years.
  • The results illustrate the power of top underwriters. The top performers each achieve 6.79 times as much revenue for this client as the bottom performers over the course of 12 years.  (This despite the same training and development being applied and available to all underwriters). This difference is worth mega $millions (purposely not revealing too much so as to protect the identity of our client).

Figure 2

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 employee’s 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.  

  • Imagine trying to convince a consumer to care about buying based on price - when their nature is to be brand sensitive.  
  • Imagine a political candidate trying to convince voters to care about a different issue. Preposterous.  

It doesn’t work. Yet we do this all the time with employees.

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.

Predicting Pre-hire Solves Attrition and Performance Problems

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).

Imagine . . .

  • Imagine finally solving the attrition problem with your high turnover, high volume roles
  • Imagine reducing your expensive, bloated support and training organizations
  • Imagine classes of new hires who have the nature and desire to perform in this role vs. people fighting your development efforts each step of the way.
  • Imagine succession plans that allow you to predict the optimal role for your top performers

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.