Use Artificial Intelligence to Set Sales Targets That Motivate

Setting the right sales targets for employees is a difficult balancing act, with long-term consequences on growth and morale.

Setting a target too low, making it easily achievable, might cause an an employee to not put in the effort. Setting a target too high can be equally problematic. “Then there is no chance of meeting it,” says Doug J. Chung, MBA Class of 1962 Associate Professor of Business Administration in the Marketing unit at Harvard Business School. “The salesperson will be discouraged, and just as unlikely to work to their full potential.”

Chung’s prime area of research lies in finding the sweet spot between these two undesirable outcomes, and determining how compensation can motivate salespeople best. In a recent Harvard Business Review article, Chung and several executives from the consulting firm McKinsey & Co explored a new way to thread that needle: using advanced analytics that incorporate artificial intelligence (AI).

In an ideal world, a company would use trial and error to set the best sales targets for employees, experimenting until they hit the right formula. In reality, that’s problematic, says Chung.

“If you want to know if a compensation plan is working or not, you need to change the compensation plan and observe and measure the change in productivity, using a control group of employees that does not experience the change,” he says. “Firms typically don’t want to do that because once you change it, it’s very difficult to change it back. Also, experimenting with a select group of employees is deemed as unfair.”

Most companies rely on past performance, setting an employee’s goal slightly higher than their sales for the previous year, a term known as “ratcheting the quotas.” But that, too, can have its drawbacks.

“Suppose I got over the quota by 20 percent, then my quota next year will be 120 percent of this year’s,” Chung says. That approach encourages employees to do only the minimum necessary to get their bonus, lest the company sets a too-high goal next year.

“An employee says I know I can do 120 percent, but I am not going to do it,” Chung says. “You are penalizing the high performers.”

Besides, a compensation plan that ratchets up slowly at a consistent rate, related to the firm’s growth objectives, only works in a stable market, in a stable industry, in a stable region. “That’s a lot of ‘ifs,’” Chung says.

Artificial intelligence—an array of approaches that rely on computer systems and algorithms to handle human tasks—allows companies to use multiple variables to compute the best targets for employees, often in real time. Many companies have started using machine-learning algorithms to construct AI systems.