How to analyze your quota distributions

By: David Eddleman Sales Quotas

Regardless of the type of business you are in or the sales strategy you set forth or how your sales force is currently performing, as the sales leader, you should always track analyze your quota distributions at the end of each performance period. And yet, shockingly, this simple exercise often gets overlooked, succumbing to the tyranny of the urgent – next quarter. In a quota seminar I facilitated recently, less than 40 percent of the executives indicated they track and review quota distributions on a regular basis. Yet hidden in last period’s performance data are insights about quota quality that the sales leader needs. Every sales leader should have a routine process for analyzing quota distributions at a granular level (i.e., sub-groups such as geographies, job roles, tenure, products, accounts, or industry verticals) to support an effective quota program. The quota program is critical to driving top line results and managing sales costs.

The Big Picture:  Normal bell-shaped quota distributions are critical to maintaining a well-motivated and cost effective sales force. Moreover, if attainment distributions are not balanced, undesirable results occur such as: low morale, increased turnover, under- or over-payments of incentives. This results in a general failure to create the desired risk/reward environment. In any given period, typically ~60 percent (and this can vary slightly by industry) of reps should achieve hit plan or greater, and ~40 percent will be below goal.

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The Smaller (and more interesting) Picture:  Frequent review of attainment at the sub-group level ensures consistent quota management across the various subgroups, which helps you to avoid potentially costly mistakes in quota misallocation. Here are 3 examples:

By Geography:  This is a common and logical first cut. Generally speaking, you should expect similar results across geographies. Factors such as market maturity (e.g., developed vs. emerging), economic growth or stability could impact quota distributions, particularly for a given period. But sustained differences in quota distributions are more often the result of inconsistent practices across geos. It’s far too common (and I suppose, easy) for sales leaders to shrug off these differences based on external factors. But they are often driven by differences in local quota allocation practices, sales crediting practices and rep deployment. The WW Sales Leader ought to examine and discuss these results with their geo leads and ensure consistent practices for quota allocation are followed.

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By Tenure:  Another great but unfortunately far too rare data cut is the breakout of attainment by rep tenure. More tenured reps often have a better chance of exceeding quota. This shouldn’t be the case. While new hires have understandable ramp up times, fully on-boarded new hires should have similar quota distributions as tenured hires. But they often don’t … because of a human problem. Left unchecked and ungoverned, managers tend to allocate less than they should to tenured, strong performers, and more than they should to new hires. The Sales Leader must watch for and mitigate this practice. Good data, as per the example below, sheds light on the situation and drives the right conversation.

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By Job Role:  Hunter, Farmer, Account Manager, Overlay, First Line Manager … Should different job roles have the same quota distributions? Maybe not, but all of these job role distributions should be normal, bell-shaped and centered around the 100 percent attainment mark. The standard deviations may be different. For example, Account Manager (i.e., “farmer”) roles in mature, well-established markets (e.g., office products) will have much smaller standard deviations (customers can only buy so many more paper clips). Meanwhile, Account Executives (i.e., “hunters”) in fast growing new markets (e.g., cloud computing) will have much larger standard deviations. Don’t try to manage the standard deviations – doing so will lead to artificially manipulating pay. Instead, test and manage for displaced, multi-modal and overall skewed results.ByJobRole_062315
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When it comes to quotas, there’s a lot a stake … a lot more than most leaders probably realize, given how few actually take the time to do the analysis correctly. Analysis of quota performance distributions yields a wide range of insights to improve the quality of your quota program and thus the performance and health of the sales force and the ROI on your sales investment. Determine which sub-groups to review based on the structure of your organization and your sense for specific problem and opportunity areas. From there, you can formulate hypotheses about root causes, dig deeper where needed, and more importantly, change how you set goals next year.

Do you analyze your quota distributions? How do they look? To learn more about how to assess and redesign your quota programs, please visit our Quotas Practice.

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David Eddleman

David Eddleman is a principal in the Atlanta office. He co-leads Alexander Group’s Sales Quotas practice. He works on a variety of issues with clients in the areas of sales strategy, operations, performance management and sales compensation. He has 20 years of experience consulting with Global 2000 companies in multiple industry verticals including high technology, manufacturing, financial services, telecommunication and aviation. David is a frequent speaker on sales compensation and sales effectiveness issues at industry associations and other organizations.


Prior to joining the Alexander Group, he was a director and consulting practice leader of the communications group at Navigant Consulting where he provided thought leadership in the areas of network strategy and competitor analysis for global carriers. Before Navigant, David was a senior manager at Cap Gemini Consulting where he led numerous projects for Fortune 500 clients both in the U.S. and in Latin America.      


David has a B.S. in mechanical engineering from the University of Maryland, as well as an M.A. and MBA from Columbia University.


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