Sales Quota Design: Using Hard Data Versus Rolling the Dice

Most organizations regard quotas as the main driver for achieving company goals and, at the same time, the first way to determine compensation for the sales reps. Its importance is heavily underestimated. You’d think companies pull in all the resources they have to set their quotas. They don’t. In my experience, most companies use a simplistic approach that often leads to incorrect quotas that demotivate the sales force and fail to drive performance.

Sales Quota Design: Using Hard Data Versus Rolling the Dice

Today’s marketplace is flooded with consumer-generated data. Companies are literally sitting on troves of information, but they are not doing enough with it. Properly managed and analyzed, data can be converted into actionable insights to set accurate quotas for your sales force. Here’s how:

Quota setting methodologies

Equal distribution, flat growth or basic trending can be calculated easily, but this will rarely produce fair quotas. Using simple methods means you are not aiming high enough, misaligning growth and goals, and misusing important business insights that your team can produce using the data at hand.

Start using business insights and go beyond the basics to calculate accurate quotas. Historical sales are a good starting point, but you also want to look at market potential, competitors, population growth, population density, median income or other demographics that are relevant for your business. Your company’s promotional spending or marketing campaigns information is also an important data point. Best of all, a lot of this data is publicly available.

Using simple quota setting methods means you’re not aiming high enough.

Don’t shy away from statistical techniques

Dealing with so much information can feel overwhelming but the results are proportionally lucrative. You can either go the extra mile and learn statistical techniques, or you can employ expertise to help with choosing the factors and weights that go into your quota calculations based on quantitative, data driven indicators. It’s the reason why SPM solutions like Optymyze exist.

Use regression to identify the importance of each factor you’re considering. And, since regression is backwards looking, modelling and simulations should then be used to confirm your insights will stay in place in the future. But it’s not all quantitative. Leaders and sales people alike have direct insight into the market. Use their knowledge and information to fine tune and finalize quotas. Getting their input will also help get the sales people and leaders engaged in the process and make them more likely to achieve their goals.

Quota setting is not an *entirely* quantitative process.

Trust the data

Once you have your process in place, stay in line with the forecast and eliminate the need to add stretch goals to protect your numbers. Instead, use a structured approach to manage ongoing adjustments related to organizational realignment, terminations, new employees, and crediting exceptions. This will also eliminate any need to for a mid-period resetting of quotas or analysis to estimate impacts.

Leveraging every piece of data to set a fair quota for everyone is an admittedly complex matter. But employing the right tools and processes makes it attainable. If I were to lay down four golden rules for sales quota design, those would be:

  • Employ agile processes (and tools) to facilitate obtaining new data on an ongoing basis
  • Allocate justly to ensure accurate compensation and results
  • Manage your data with discipline but at the same time staying flexible to change
  • Assess often, do not just once a year

So, are you ready to use hard data when setting your quotas?

Emilian Romas

Professional Services Manager

Emilian is an outcomes focused professional with 11 years of sales performance management, business intelligence and analytics consulting experience.

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