A top down look at quotas and sales coverage

A top down look at quotas and sales coverage

It is our experience that up to 10% of sales cost is overspend resulting from a sub-optimal combination of sales coverage, quotas, credit, and incentive design, with the most significant impact from coverage and quota. Here, we’ll explore common drivers and what a company might do to enhance coverage and quota setting processes. 

Past is prologue…

The SEC requires mutual funds to include a statement in every prospectus that past performance does not guarantee future results. The implication is a simple one – don’t make investment decisions based on history. However, when companies make decisions about where to invest their sales resources, the past is prologue. Sales reps are expected to grow sales from last year’s baseline on the same set of accounts.

The most common approach for setting quotas is top-down, where the corporate forecast is cascaded down a sales hierarchy according to the historical contribution percentages of accounts. Historical performance data is the predominant input into most quota setting processes.

Because it’s there…

George Mallory famously responded when asked why he wanted to climb Mount Everest, “Because it’s there.” We often find a quota is allocated to an account ‘because it is there’, it must have a quota because it exists in the database and has an historical value associated with it. However, setting quotas for inactive accounts, or simply where no sales effort is planned, contributes to irrelevant quotas and sales coverage issues.


Sales Managers are asked to adjust quotas based on their knowledge or intuition. This is like predicting the stock market. Though it may feel right to say “Sales Managers know because they are closest to the accounts”, managers often do not have the tools required to make informed data-driven decisions. They can identify past anomalies (such as a one-time deal skewing historical data), but they cannot use intuition to accurately predict future sales[1].

We find that correlation between current period and last period’s revenue is pretty high, R2.70, quotas are correlated about R2 .50, and attainment is not correlated, R2 .006. If quotas were simply set on last period’s production plus a flat 10%, performance would be highly correlated. In other words, quotas set on history alone are more accurate than quotas set on history plus manager intuition. Nobel Laureate Daniel Kahneman says it best, when in doubt, it’s better to trust a computer algorithm.

Overspend areas…

Companies incur both real and opportunity cost related to their quota and coverage processes. Missed opportunities due to under-served accounts, higher cost due to low quotas or excess crediting, even attrition as sales reps are discouraged by irrelevant high quotas – all come as a result of the misalignment between top down processes and opportunities.

Finding a ‘rithm…

The most effective approach for quota setting is the use of account planning, coverage rules, and algorithms. Algorithms should take into account several factors, such as install base, correlating products, product lifecycle, buying power, and more, enough factors to understand and define an opportunity set for a given time frame.

This is not to be confused with trending (linear regression, exponential, etc.) which uses using historical data to predict future performance. Trending is often used as the basis for quota setting, to give a ‘bottoms up’ view, but the net effect is the same, the approach does not take into account potential sales drivers or account plans. 

How do you quota?

Algorithms and rules based coverage models can help companies define opportunities and more effectively allocate sales resources where they will generate the best return on investment.

What challenges does your company face in setting quotas and optimizing coverage models? What algorithms and coverage rules does your company leverage?

[1] Dworschak, Manfred and Johann Grolle. Psychology: “Debunking the Myth of Intuition.” Before It’s News, 3 Nov. 2012. Web. 30 Sep. 2014.

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