Joe DiDonato | Chief of Staff | Baker Communications
Ten years ago, if you asked me to trust data science to help me make hiring decisions based on data, I would have probably laughed. Like everyone else who was using personality and culture assessments to help them make these decisions, there was no real link between being a fit, culturally, and job success. It may have been fun to go out to lunch together, but that’s about the extent of their predictability.
However, in recent years, the predictive validity of tests in the sales arena has really soared. They are now between 91-96%. In fact, if the data is telling you to hire the person, that seller has a 92% chance of making it to the top half of your sales team in 12 months. And conversely, if the data is telling you not to hire a salesperson, and you ignore that, you’ll find 75% of those hires leaving in the first 6 months. Just between you and me, that’s a ‘heck’ of a lot better than I was doing a decade ago.
So, here I came “kicking and screaming” into the world of data science. And now that I see how you can tune these assessments by job role and by industry, I’m fully converted. The ability to compare a candidate’s competencies to the top performers in the world, in that job role and industry, is what’s changed the game for me. At the time of this writing, in one assessment company alone, over 2.1 million sellers at 32,800 companies have taken these assessments. They run across 200 industries and 141 countries as well.
So why the resistance? Maybe it’s change. Maybe it’s the fact that somehow, we’re feeling left out of the loop. Maybe we think we can do a better job.
But the facts are pretty clear when you look at average turnover rates in the industry. It’s more than double any other job role, at 34.7%. That’s a bit on the embarrassing side if you ask me. And on top of that, two-thirds of that percentage is a result of “involuntary turnover.”
The problem is that our “people-pickers” are not functioning at peak performance when it comes to filling the sales role. And we’re having to replace one out of three people we hire. Maybe sellers are better at interviewing because they know what to say when asked questions like, “Do you consider yourself a people person?” “Do you understand the importance of customer support?” And so on. If you ever operated a successful lemonade stand, you could probably figure out the right answers.
So now we’re back to the cost side of things. Replacing 35% of your sales team each year is going to be pretty expensive. Average recruiting costs are $29,000, and average training costs are $36,000. But that’s not the biggest expense. It’s the time it takes to replace a salesperson in a territory that takes the biggest toll. It takes between 5.8 to 7.8 months to replace an individual in a territory. If there are accounts to be managed and trust to be rebuilt, that number can take even longer.
If your quotas are in the $100,000 range then that’s not going to add too much more to the cost. If you lost 6 months then your “lost opportunity” cost is $100,000 x 6/12ths (or 50% of the assigned quota), or $50,000. Add that to the $29,000 acquisition cost and the $36,000 training costs, and your out-of-pocket is going to be $115,000. If you have a 100-person team, then your cost to replace 35 of them each year is 35 times $115,000, or $4.025 million. That seems like a lot considering 100 people carrying $100,000 quotas are expected to produce $10 million. Imagine having to replace a $1,000,000 seller. You can do the math.
So, why not consider using data science to help lessen these problems? It seems like a no-brainer at this point to run all of your candidates through these assessments. Not only do you find out if they’re a good choice, but you also get to learn all of their strengths and weaknesses before they come on board. With that kind of information, you can really take a pass at shortening the onboarding time and getting the new person into the field selling. All you really need to do is map skill gaps to training. If a weakness is uncovered that isn’t a trainable skill, then as a sales manager you have a coaching handbook handed to you.
Well, it’s your business to run, so you need to make the call. But consider this: If you went to a doctor who didn’t believe in using lab work or x-rays or MRIs to help diagnose your medical problems, what’s your confidence level in his or her medical opinions about your health and prognosis?
If you’d like to learn more about how to use this predictive data to drive your hiring, onboarding, training, and coaching decisions, we invite you to listen to the advice and outcomes of a sales executive who changed her entire hiring and training process over to the data-driven approach. Watch the video here: https://www.bakercommunications.com/tailoredfit.html.
