Modernizing Sales Incentives for Optimal Revenue Results

Sales is one of the world’s oldest professions — dating as far back as the business of exchanging goods and services for something of value. In modern times, the classic cycle of initiating contact with a prospect, discovering their needs, presenting solutions, and closing the sale has remained largely unchanged.

Equally consistent has been the way in which sales professionals are incentivized and compensated. Traditional compensation plans look very similar across most sales organizations — a combination of base salary and variable pay where the variable portion is a reward for successful quota attainment. Sales reps work toward revenue quotas over set time periods (e.g., monthly, quarterly, annually) and receive their commission payments on a similar cadence.

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If the whole industry follows this approach, it must be a winning one, right? Not quite. A deeper look at sales performance will often show that money is left on the table because of suboptimal, incentive-driven behavior. While it’s common for lost revenue or missed goals to be blamed on lack of productivity or gaps in training, many organizations overlook the real problem: how reps are incentivized and rewarded.

Why Traditional Sales Incentives Are Broken

At its core, the sales incentives problem is a psychology problem. In the traditional compensation approach, commission payments occur in the distant future, but real-time decisions and actions are happening today. Humans notoriously struggle with making decisions in service of future outcomes. For instance, many people make an annual New Year’s resolution to get in shape but then fail to make the right day-to-day decisions to achieve that goal.

Sound familiar?

Traditional sales incentives create a similar delay between action and reward, resulting in suboptimal behaviors that negatively impact results:

Poor prioritization: Sales reps tend to react to the customers that are the loudest and have the most immediate needs. They focus too much on smaller, close-to-the-money deals and not enough on the larger deals that have a longer lead-time and may not close but are highly valuable to the business when they do.

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Risk avoidance: Traditional sales compensation is all or nothing. The sales rep endures all of the risks when investing time and effort into a deal that may not close. If the deal is ultimately lost (for reasons that could be outside of their control) or the rep leaves the company before the deal is closed, they get nothing even though the great effort may have been invested. As a sales rep, your incentives tell you to minimize risk and take the easier wins, even though spending time on bigger deals might be best for the overall business.

Driving strategic priorities: Traditional compensation plans are complex and hard to update. While generating revenue is an evergreen objective, strategic priorities (such as focusing on selling a particular product, or to a certain set of accounts) come and go. Quickly pivoting to reward unique objectives is difficult to do in a meaningful way with a standard revenue quota.

Even though rewarding progress is an obvious solution to the actions-to-rewards time delay, historically, it’s been impractical to do so in an objective way. Many teams track activities such as calls made or meetings held, but these inputs don’t necessarily equate to outcomes. Achieving milestones in a sales framework or moving a deal through stages would be better measuring sticks, but are subjective and often self-reported. Tying rewards to an activity or self-reported data can create a whole new set of bad sales behavior (often worse than the original problem).

Machine Learning-Driven Incentives

While traditional sales compensation systems are built around periodic quota attainment, advances in machine learning now make it possible to objectively measure true deal progress in real-time. Here’s how machine learning-driven incentives work: the underlying machine learning technology measures the likelihood of a deal to close. As that likelihood increases, the sales rep is rewarded. This approach motivates sales reps to invest more effort in the high-value, longer-term deals and rewards them for generating positive progress when they do.

The market for machine learning-based sales technology is growing as organizations look to get an edge in a highly competitive industry. Across applications, machine learning has the ability to analyze tremendous amounts of data rapidly — a task better suited for computers than humans. The machine processes large sets of sales data (customer information, sales rep activity, voice/email communications, historical results, etc.) to understand what drives both good and bad outcomes, then produces useful insights to enable sales reps, managers and executives.

Equipped with machine learning technology, sales teams are able to focus more of their time on what they do best — sell to customers. With the help of machine learning, sales leaders can seamlessly augment their team’s incentives programs and reward the right behaviors. With the right “nudge” in place, sales reps will naturally adjust their behavior and stretch toward their true revenue-generating potential, leading to stronger personal performance and improved business results for the organization.

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