Major League Innovation

by Scott D. Anthony

IT’S OCTOBER, AND IF you’re an American sports fan, you’re probably choosing sides in the upcoming World Series—the culmination and champion crowner of Major League Baseball’s seven-month season. But are you reflecting on what you as a manager could learn from the winning team?

When you think about it, a corporate executive on the hook for delivering growth-fueling innovation has much in common with a ball club’s general manager. Both are constantly shifting their lineups, making decisions to add or prune under high degrees of uncertainty. Both have stakeholders demanding immediate results, but also deeply appreciate that history will be the judge of their true legacy. Vindication for wise choices sometimes doesn’t come for years, and even the best leaders lose more games than they expect to along the way.

Is it possible that the similarities extend beyond the challenges? Could the ways that general managers solve their problems translate to business? In at least three major areas, it seems so. If you’re an innovation manager struggling to predict the success of potential new offerings, develop promising ideas, and assemble a balanced portfolio of growth initiatives, the best kind of inspiration may come from the nearest ballpark. (Whether you expense the ticket is your own affair.)

Discover What Really Indicates Success

Famously, the Sabermetrics revolution in baseball (named for its basis in research conducted by members of the Society for American Baseball Research, and described by Michael Lewis in Moneyball) has taught general managers and fans how to zero in on the real predictors of performance in the big leagues and the decisions that really win games. As an executive managing a portfolio of innovation initiatives, you need to develop the same willingness to go beyond long-held assumptions and simplistic metrics.

Consider the conundrum presented by Jeff Bagwell and Jeff Ballard, who have little in common other than their given names. The former was a slugging first baseman who had an illustrious career. The latter was a pitcher whose performance was lackluster—save for one sterling season. Obviously, Bagwell would have been the better player to bet on, but could a general manager have predicted that early on?

Not if, as was long the practice, he overlooked minor league statistics. Given the difference between the majors and the minors in level of play, talent scouts traditionally dismissed the outcomes of minor league games as almost meaningless. Instead they looked for fundamentals—the physical attributes and skills that equip a player for high-level competition. Few complained, therefore, when in 1990 the Boston Red Sox traded a 22-year-old Bagwell to the Houston Astros for an aging left-handed reliever. Sure, Bagwell had racked up hits in the minors, but he wasn’t much of a third baseman (his position at the time). Worse, he was squat and had an unorthodox swing.

Bagwell went on to slug 449 home runs during his career, and even collected the National League’s Most Valuable Player award in 1994. The few students of the game who had seen his potential, such as the baseball historian Bill James, spotted it because they’d devised some simple algorithms to translate minor league statistics into major league equivalents.

Moreover, today’s analysts would instantly have recognized that Jeff Ballard’s pitching performance in his breakout major league season was an aberration. (Ballard did have superficially good minor league stats, but deeper analysis of his hits allowed, walks, and strikeouts demonstrates that his results came from good defense and luck—not sustainable skill.) Few observers would have agreed as they watched the 25-year-old left-hander lead the Baltimore Orioles—who had lost their first 21 games in the previous season—to the brink of the playoffs. In 1989 Ballard won 18 games, had a 3.43 earned run average, and finished sixth in the Cy Young race for the American League’s best pitcher. He went on to win 13 more games … in the rest of his career.

The implication for business innovation managers is that a more insightful analysis of available information can overturn orthodoxy and inspire better tactics, investment decisions, and personnel management. Perhaps the scientific approach will never go quite so far in the realm of management; baseball, with its discrete, largely independent events, is particularly well suited to it, and the data to support similar statistical analysis in innovation management simply don’t exist.

The innovation manager isn’t helpless, however. Research by Clayton Christensen, Robert Burgelman, Vijay Govindarajan, Henry Mintzberg, Rita McGrath, and many others continues to highlight historical patterns of success that can make the market performance of a given innovation more predictable—and that challenge the conventional wisdom about how to choose among competing investment proposals. (See the sidebar “Sizing Up an Innovation’s Potential.”)

Build Your Organization’s Depth Chart

A depth chart reflects a team’s level of investment in different areas—the bench strength backing up every position. It signals much about strategic priorities, such as where risks are perceived to be highest and redundancy most necessary. Companies should think in the same ways about their innovation portfolios. Do they have a balance of offensive and defensive strategies? Are they consciously exploring new channels or geographies? Do they have ideas with meaningfully different strategic intents, such as creating new growth platforms rather than extending current ones, or marketing based on features or functions rather than on packaging and promotion?

Of course, a baseball depth chart begins with what everyone agrees is the right dimension on which the team must be diversified: its player positions. It’s impossible to imagine a general manager’s saying “I’m really happy with our team’s depth—40% of the players are from Latin America, 30% are from Asia, and 30% are from North America.” It’s obvious to everyone that the real question is whether the team has sufficient coverage of the pitcher’s mound, first base, the outfield, and so forth. Further, general managers seek players who throw with different hands and have varying skill bases, such as speed or power.

For corporate managers, what constitutes a usefully diverse innovation portfolio can be less clear. Too often, as they field one close-to-the-core line extension after another, they persuade themselves that they are diversifying, perhaps because the new offerings target quite different consumer segments. Yet these managers may be missing a more important dimension across which they should be spreading their bets. For example, is it wise to design all innovations to move through the same channels? (Could P&G implode if Wal-Mart stopped selling branded products?) Don’t be unprepared for what might seem to come out of left field.

Develop Potential in the Minor Leagues

Baseball’s farm system holds lessons by analogy: Most professional ball clubs oversee several levels of teams, and even the best prospects spend a few years in the lower levels before heading to “the show.”

One rationale for the multitiered system is that talent of a major league caliber isn’t always obvious when a player is young. The annual major league draft goes on for dozens of rounds. Although most great players are snapped up in the first few, teams sometimes land talent late in the draft. For example, in 1988 the Los Angeles Dodgers selected Mike Piazza in the 62nd round. Some suggested that he’d been chosen only because his father was a childhood friend of Tommy Lasorda, then the Dodgers’ manager. Piazza turned out to be one of the great batting catchers in the history of baseball, hitting 0.308 with 427 home runs in his 16-year career.

Not only does an extensive minor league system allow teams to identify the players with sufficient talent to perform at the major league level, but it allows coaches to work with players to address identified limitations in lower-pressure environments.

Let’s apply this analogy to packaged goods companies. For them, making it to the show means scoring scarce shelf space in a large retailer like Wal-Mart. Such retailers can be brutally discriminating. If in its first few weeks a new product doesn’t appear capable of selling well enough to earn that real estate, its career is over. But research on innovation shows that almost nothing springs perfectly formed from the heads of designers; about the only thing you can count on is that you won’t get the strategy right on day one. Exposing ideas to the mass market too early increases both the likelihood of failure and its economic impact. It doesn’t provide room for learning to occur.

Develop potential in the minor leagues

What would it mean to create a minor league system for product and service innovations? To be sure, companies have long made use of test-market research and regional rollouts. The key is to deliberately organize such activities into a system whereby new offerings face steadily increasing levels of scrutiny from prospective customers and become stronger in the process. Procter & Gamble, for example, has made a recent effort to test very-early-stage ideas in low-stakes but revealing market settings. The company distributed a potentially game-changing baby care product to a handful of consumers at an amusement park and then began selling the product over the internet. It opened a small store in Ohio to test Swash, a line of fabric-care products targeting the 30% of garments that are reworn without being laundered. It opened three stores in Kansas to test the potential of Tide-branded dry cleaners. P&G’s chairman, A.G. Lafley, is a strong believer in this kind of staged learning. In a 2008 discussion he told me, “I’ve become a pretty big believer in getting the idea or technology to some relatively clear concept expression and some relatively crude prototype as fast as you possibly can, and then [getting] that in front of prospective customers.” Like any great general manager, he knows not to launch new players into the big leagues without proof points along the way.

Originally published in October 2009. Reprint R0910c