Companies that use a scientific approach to sales force effectiveness, have found that reps in the lower quartiles show dramatic improvement, with productivity jumps of 200%.
Imagine this little fable is you:
Bob Brody leaned back in his chair, frowning. Corporate wanted another 8% increase in sales from his division this year, and guess whose shoulders that goal would fall on? Ah, for the good old days, when he could just announce a 10% target, spread it like peanut butter over all his territories, then count on the sales reps for each region or product line to deliver. Sure, some would fall short, but the real rainmakers would make up the difference. Today, the purchasing departments of Bob’s customers used algorithms to choose vendors for routine buys; pure economics often trumped personal relationships. For more complex sales, purchasing wanted customized end-to-end solutions. There’s no way one person could close those deals, no matter how much golf he or she played. Most of the time, you needed a team of product and industry experts, not to mention rich incentives and a lot of back-office support.
The fact was—he knew he’d have to face it sooner or later—Bob was overwhelmed. Nothing about the sales process was as simple or predictable as it used to be. Eight percent growth? He wasn’t even sure where to start.
Today, the savviest sales leaders are dramatically changing the way they run their groups. They are reinventing their sales approaches to respond to new market environments. They are expanding their lists of target customers beyond what anyone had previously considered. They are boosting their sales reps’ productivity not by hiring the most-gifted individuals but by helping existing reps sell more.
What these leaders have in common might be called a scientific approach to sales force effectiveness. It’s a method that puts systems around the art of selling, relying not just on gut feel and native sales talent—the traditional qualities of the rainmaker—but also on data, analysis, processes, and tools to redraw the boundaries of markets and increase a sales force’s productivity.
The goal isn’t to replace rainmakers but to narrow the gap between the top 15% or 20% and the rest of the sales force. Companies that use the tactic well have found that, while even top sellers do better, reps in the lower quartiles show dramatic improvement, with productivity jumps of 200%. Such increases enhance the performance of the sales team as a whole and enable a company to reduce the expense of hiring new reps. Some firms using the approach have seen their average sales per rep increase by as much as 50% in two or three years, though most gains cluster around the 30% mark.
Putting Science into Sales
GE’s Pilot understands how extensive a reinvention can be. As recently as the mid-1990s, the company was still expecting sales teams to assemble and prioritize their own database of prospects for their territories. The company’s field sales managers even manually classified all the names in the division’s database as either high priority or low priority. “We relied on telephone books,” recalls Pilot. “And newspapers. And signs on trucks as they went by or signs on buildings.” By 2004, says Pilot, he knew that GE Commercial Finance had to “put some science into it.”
Pilot’s first step was to revise the way he segmented customers—by using data that included records of past company transactions. The new database held information such as four-digit standard industrial classification codes, the type of equipment being leased, and so on. Then Pilot asked his field managers to create a list of prospective-customer characteristics, criteria that they believed would correlate with a customer’s likelihood of doing business with GE. He took the 14 features they came up with, ran regression equations against the database of transactions, and identified six criteria that had high correlations. If a prospective customer tested well on those six criteria—such as predicted capital expenditures and number of filings for new business transactions—the probability that it would do business with GE was high.
The division scored its list of prospects based on the six attributes and then worked the new list for a while. Something interesting emerged. “We found that the top 30% of prospective customers were three times more likely to do a deal with us than the bottom 70%,” says Pilot. In other words, that top group was made up of the new highest-priority prospects—and yet only about half of them had previously been classified as high priority by sales managers. The company had, in effect, identified 10,000 new high-priority prospects that it would otherwise have overlooked.