Good distribution

This piece follows on from some points I made in my previous post, a quibble with quotas, about what the ideal performance distribution really is.

In case anyone is unfamiliar with the concept, basically what we are looking at is how a group of sales people perform against their target or quota. Do most of them overachieve? Do most of them underachieve? Is it about 50:50?

A word on stretch

It’s amazing how many companies don’t truly understand how much stretch is baked into their reps’ quotas. In one project I have worked on, finance created a target which was stretched by 10% and given to the sales director. The sales director adds their own stretch of another 10% and hands this down to the sales managers. The sales managers then add anything between 5% and 10% on before giving individual quotas to their sales reps. That’s potentially over 30% of stretch from the financial targets!

Of course, adding stretch isn’t wrong and should happen to some degree in all companies. What is critical though, is to understand the impact and to make sure that performance distributions are interpreted with this in mind. Whatever the philosophy around performance distribution, it may be necessary to either remove the stretch from quotas in order to accurately reflect and assess the performance of the sales force, or adjust the target distribution (and rate changes) to accommodate.

Philosophy

A common starting philosophy is that about 50% of sales reps will make target and about 50% will fall short. In other words, a normal distribution curve around target performance. This assumes that the company sets a reasonable target and on average the sales force will achieve that target, meaning that there needs to be an equal spread of winners and losers.

At the other extreme, an organisation may have the belief that their sales team only exists in order to hit target so they should be hitting it. In this case, 70% perhaps 80% of the sales force would be expected to achieve target with the remainder regarded as under-achievers. To some this may seem cut-throat but it isn’t that unreasonable when you think about it – why do we want sales people that can’t hit their targets?

The right philosophy will be a reflection on the company’s views around sales performance, but should also reflect how attainable those targets are. As an illustration, with a 30% stretch built into the individual quotas and a view that 80% of the sales force should be achieving target, the sales force would have to achieve at least 150% of the sales target on average in order to achieve the overall financial target. Fortunately such extremes are rare.

Setting the points

With a target distribution decided, the next step is to decide on the threshold and excellence points.

This is such a common query – “What should the threshold be?” “What should the excellence point be?” “What are other companies doing?” As I have spoken about previously, market data should be used with caution when it comes to designing sales plan mechanics.

I would always encourage companies to think, not in absolute terms, but in relative terms about what proportion of their sales force should be classed as “Excellent”, and what proportion are simply not good enough to warrant a pay-out. Market data often shows 75%-85% of target to be a common threshold. If the lowest performer in a company consistently achieves 90% of target because of a strong recurring base and good products then, in this case, the market data is basically useless – what’s the point in setting a threshold that everyone hits?

At the other end, what proportion are “Excellent”? A top sales sales person will regard anything less than hitting the excellence point and making the president’s club a bad year. 120%-140% are typical performance levels that unlock the excellence point, but if only 5% of our organisation can achieve it, are we demotivating our top performers?

In “Straight from the Gut”, Jack Welch famously advocated the 20-70-10 system where the top 20% of the workforce is most productive and the bottom 10% are “non-producers”. Jack’s approach of firing the bottom 10% each year may be a bit strong, however this is often a good starting point for discussion. Others prefer the symmetry of a top 10/bottom 10 approach.


Using distribution to set points on the curve is critical to getting the most out of a sales force. Once this has been decided, this aspect should be monitored closely as part of the scorecard to ensure that we are rewarding the right people. Good distribution is key. As you can see here:

Great distribution
Now that, is good distribution.

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