# Proving Math Works, Flawed Coaching Arguments

There seem to be no end of articles on who and how managers should invest their time in coaching. Recently, I read another, with lots of people piling on with their opinions and proving it with numbers. As is usual, most of these discussions that prove little more than math works.

Many of the arguments go something like this: “Assume an A player is doing $250K, a B player is doing $150K. If we raise the performance of each by 10%, clearly the incremental $25K from the A player far exceeds the increment of $15K from the B player.” With that argument, we are all supposed to see that we should be investing in our A players. Clearly, if we have only 2 people to manage, that makes sense, but then one wonders, why not coach both?

But usually, we have more than 2 people on our teams. The average span of control is 8-14 people. So let’s re-rerun the math. Let’s assume a “normal distribution.” We have 2 A players, 6 B players, and 2 C players. We can still play the math games, but this time with a different conclusion. With a 10% improvement in the A and B players (we’ll get to the C players later), the net improvement driven by A players is $50K and the net improvement driven by the B players is $90K. Using the same math and logic, we have a completely different conclusion.

Let’s then think about the assumptions, is it realistic to assume the same 10% improvement in each category of sales people–which all these math arguments assume? If an A player is playing at the top of their game, is it reasonable to think that we can get a 10% performance improvement, just because we say so? If they are highly effective, impactful, and productive, certainly they can improve, but 10% might be a stretch, or it might take much more time to get to that level.

As much as I hate sports analogies, think of a top performing athlete. While they continuously improve, the rate of improvement tends to decline because it becomes more and more difficult to find and achieve those levels of improvement. It’s the same with our top players. They will and want to continue to improve, but the increments will decline because it’s much more difficult to find those improvement opportunities.

Likewise, with the B players, why have such low expectations of them with only 10% improvement? Clearly, at the original baseline, A players are performing 66% better than B’s. So we have the opportunity to improve their performance to match the A players by as much as 66%, why are we setting arbitrary assumptions limiting ourselves to 10%? And the path to that improvement is pretty clear, we model the A player approaches and coach the B players on those already proven approaches.

But again, I’m just playing with numbers and since I am setting the rules, I can do anything to prove my point, and math always works.

We always tend to ignore the C players in these discussions. (Yeah, we want to ignore them, they are such a pain to work with and coach.) Let’s say our C players are producing $100K. Sure we can use the same arguments, let’s improve their performance by 10%, getting them each to $110K. It’s far less than the $25K improvement from each the A players. Less than the $15K improvement of the each of the B players, but usually, we apply this same logic.

But then, we think again. Because they are C players, how easy is it going to be to get them to increase their performance by 10%? (This is actually the same challenge with increasing the performance of A players by 10%).

On the other hand, what we miss is the devastating impact they have on revenue. We are losing a minimum of $100K on those two C players (They are each performing $50K less than the B players), or possibly $300K when compared to the potential that A players show we can achieve. This would argue, we should be investing the majority of our time in the C players because of eliminating the lost revenue they create is far better than the investments we make in either the A or B players.

We can play these numbers games all day. In this short discussion, we’ve gone from saying, our highest leverage is the A players, to the highest leverage is the B players, to the highest leverage is the C players. But all we’ve really done is played games to prove math works.

When are we going to focus on the basic responsibility of Front Line Sales Managers and stop playing silly math games?

The job of the FLSM is to maximize the performance of everyone on their team.

We can’t work with just the people we want to work with, we have to work with everyone. We can’t invest equal amounts of time in each person, we have to invest the right time in each person. Because each person has differing needs, each has differing strengths and weaknesses, we have to adjust the time and coaching approaches to each individual. The rate of improvement of each person will vary, but we have to maximize each the performance of each person.

The job of the FLSM is, also to maximize the share and growth of the territory. As a result, we have to continue to look at the mix of our people to determine if we are doing everything we can to get the most out of the territory. If we can’t raise the skills of our people to achieve our goals in the territory, we need to look at remaking our teams, building them with people that enable us to achieve the full potential of the territory.

I suppose it’s human nature to want easy solutions, but simplistic arguments aren’t helpful.

Let’s stop playing silly math games, let’s start focusing on the jobs of FLSMs, coaching them, giving them the tools necessary to maximize the performance of their teams and their performance in their territories.

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