I’m a Major League Baseball fan and an engineer by education. These two passions of mine have intersected as baseball has transformed from a game dominated by intuition, experience, and a few metrics to one where analytics have leveled the playing field, and helped teams in small markets, with low payrolls, identify talent, and compete with their higher-payroll opponents. While October is the most exciting part of the season, November and December are fascinating from a business perspective as teams vie for top talent in the free agent market. You may have read “Moneyball: The Art of Winning an Unfair Game” by Michael Lewis or seen the movie. The book outlines how a forward-looking general manager, Billy Beane, embraced analytics to make the Oakland Athletics a perennial competitor. Some view this as a baseball book, but most recognize that it’s a story about business and an astute leader. Forward-thinking sales leaders will follow Beane’s example and use data and analytics to make more calculated decisions across a wide spectrum of sales effectiveness areas, from segmentation based on deep customer insight, to sales coverage based on customer opportunity, to enhanced sales process execution focused around critical selling activities. These leaders will also use data and insights to make better hiring choices, manage performance through greater visibility into key metrics, and set realistic sales quotas that reflect the addressable opportunity and drive true pay-for-performance. Beane did not transform the Oakland A’s on his own. He was supported by a group of analysts led by a Harvard-educated economist, Paul DePodesta. Similarly, sales leaders will turn to the sales operations function to discover new data-driven ways to add value and increase impact. In addition, sales operations will develop and implement methods of delivering insight in a manner that is relevant and timely through the use of mobile applications that improve data visualization. These enhancements are illustrated by the following use cases:
- Segmentation based on deep customer insight: Most B2B segmentation schemes today tend to be firmographic (i.e., company size by number of employees or revenue, industry, geography, etc.) What if sales leaders had insight on why customers buy and their likelihood to buy?
- Optimized sales coverage: Today, coverage is primarily based on geographic coverage or product specialization. What if that was enhanced by understanding relationships? Though sometimes relationship indicators can be superficial (think about number of contacts in LinkedIn.) What if we could assess the depth of engagement and degree of influence?
- Hiring decisions: Prospective salespeople may be judged on size of their Rolodex. What if we could evaluate salespeople based on the strength and quality of their connections?
- Pipeline forecasting: Many sales leaders miss their projections or commitments, sometimes in a good way, but often fall short, even when their opportunity management systems show robust pipelines. What if sales forecasting was enabled by analytics that used prior history by salesperson, customer, and/or solution area to forecast probability versus what is hard coded by sales stage or estimated by the sales team?
- Quota decisions and resource allocation: How often do you see the peanut butter approach—10% increase for everyone? What if we could understand potential at an account level, by solution area?