You hired a new account executive (AE) on July 1st named Sam. It is October 1st and Sam is sitting in your office ready to discuss his first-quarter performance. You, as his manager, are trying to figure out if his lackluster quota attainment is because he is new, or because he does not have the right skills. You decide to trust that your hiring process worked and that Sam is a skilled salesperson. It is just taking him a few months to get going.
Fast forward to today, you are now halfway through Sam’s second quarter and he is still not closing deals at the same rate as your veteran sales reps. How do you evaluate Sam’s performance and decide if you are going to keep him next year? Your sales ramp rates will tell you.
In a recent ORM blog post, we discussed the hidden costs of sales team turnover.
One of those hidden costs is the impact of ramp rates on new salespeople. After publishing that post, several people have reached out to ask me about sales ramp rates. What are they? Why should I care about them? How do I use them? We will explore each of these questions to help you get a sense of how important sales ramp rates are to managing your sales team.
Sales Ramp Rate Components
A sales ramp rate has two components: time and expected quota attainment.
The time element refers to the months it takes Sam to go from your new hire to a fully-productive AE. Expected quota attainment is how Sam should perform during the months it takes him to become fully productive. If you sell complex enterprise technologies the ramp time can easily be 18 months.
A reasonable quota attainment schedule might be: 20%, 30%, 50%, 65%, 70%, and 95% of quota over the first six quarters. You should note that a fully-productive sales rep might not (on average) achieve 100% of his quota.
Why Care About Sales Ramp Rates?
First and foremost, the turnover is expensive. You need a framework to quickly evaluate how your new hires are performing. This will keep you from prematurely letting someone go and will keep you from overinvesting in those who will not work out. Just as important, you need to have a reasonable expectation for how your new hires will perform so you can accurately plan for their contribution to revenue.
Here is an Example
Consider Sam. He has an adjusted annual quota of $580k. When you spread that quota over the last 6 months of the year and adjust for seasonality you get the green line (100% Quota Attainment) on the graph below. You cannot reasonably plan on Sam producing $290k in orders over his first 6 months, but what should you expect? (Note that orders refer to a commitment from a customer to spend money with your company.) If we use the quota attainment schedule identified earlier you end up with an adjusted expectation for Sam of $68k. This is denoted as the blue line (Personal Expectation).
You now have a framework from which you can evaluate Sam’s performance and determine a reasonable expectation for the orders he will produce. Your hope is that he is performing to your expectation. Sam’s actual orders are denoted in black (Actual Orders). Sam has produced $71.5k in orders since he was hired. When you compare his performance to your expectation he is doing well. November looks a little low, but the month is not over, so there is no need to worry.
It looks like Sam is on track and you should keep him on your team next year.
What to Conclude
Sales ramp rates are key metrics to consider when managing your sales team. To calculate them you need to consider the historic performance of each new hire. It is important to consider each sales position as well as any expected improvements due to better onboarding or training. These calculations can quickly become a complex spreadsheet exercise, so you should consider a sales analytics package to help.
If you would like to learn more about how ORM Technologies can help you with your ramp rates, email us at email@example.com.
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With over a decade of experience with advanced technologies, Pete leads both the Sales and Marketing teams at ORM Technologies. Previously, as the VP of Engineering, he led the technical development of ORM's advanced analytics and optimization solutions.
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