How Should an Organization’s Overall Sales Goal Be Distributed Across Salespeople?

It’s important to distribute your organization’s overall sales goal across salespeople using a data-driven quota model. Building a data science algorithm that factors in recent trends and what has happened historically in the territory assigned to each salesperson is a great way to this in a robust manner. We would highly recommend avoiding letting individual sales managers set quotas for their direct reports as this can open the door for the potential to let favoritism influence the allocation. Even if this weren’t a concern, the data-driven quota modeling approach will almost always more accurately prescribe a reasonable goal for each salesperson based on their territory makeup than a sales manager can.

It is common for organizations to over-rely on last year’s sales for the same time period coming up when setting a territory’s quota. While it’s true that seasonality can have a big impact on sales and that starting with the same time period last year is one way to account for that, this simple method fails to account for the fact that some territories may have seen some dramatic (non-seasonal) growth over the last year. If the quota is based on sales volume from a year ago in territories like these, a salesperson may essentially be guaranteed to hit their quota based on their run-rate from the past few quarters. Keep in mind that the salesperson currently in that territory may have recently inherited it and may not have even driven that growth over the last year. The opposite scenario is also problematic.

A robust quota model should essentially suggest a quota based on what the typical salesperson would be able to do with that territory based on the customer makeup, trends, and what has historically happened next when those trends have been seen (which may not be a continuation of the same trend!). For instance, after building a quota model on historical data, the model may find that customers seeing exponential growth in one quarter typically level off a little in the next quarter (rather than continuing to see more exponential growth); this insight would then be baked into the sales expectation for any customers who have seen exponential growth over the most recent quarter.

Another benefit of creating a data-driven quota model is that your organization gains performance granularity down to the customer level; so, if desired, an organization can explain exactly which customers a salesperson is overperforming on and with which they are underperforming. Value Driven Analytics can build a rigorous salesperson quota model for your organization that ensures a fair distribution of your organization’s overall sales goal.