By Guest Contributor, John Warner | CEO, Innoventure
Wouldn’t you jump to use a powerful analysis tool allowing you to predict business success with a high degree of accuracy?
Join me in a thought experiment. Imagine participating in the October 1969 strategic planning retreat of Sears Roebuck. Executives are reviewing their plans to roll out mall-based stores in affluent suburban markets. The suburbanization of America will generate growth as far as can be seen on the horizon. As a retail powerhouse, Sears has information dominance over its rivals, including point-of-sale data and customer focus group research, as well as relationships with most mall landlords in the country and global vendors. Bursting with confidence, Sears’ leadership reviews plans for their iconic headquarters, the Sears Tower in Chicago, which when complete in 1973 will accommodate their headquarters expansion to support the world’s largest and most successful retailer.
During a brainstorming session at the retreat, a young manager with a Southern twang suggests Sears can be successful expanding into poor, rural Southern markets. Sears’ leadership has an operationally excellent culture built around making data-driven decisions. The Sears store location team is asked to use the demographics for Bentonville, Ark., to assess whether Sears can be successful there. After cranking the demographics through the Sears model, the location team reaches a clear conclusion reported to senior management: “You can’t make money in Bentonville.”
Unknown to Sears’ leadership, at the very time of the retreat, a middle-aged entrepreneur was incorporating a retail chain he had founded only seven years earlier which had grown to 24 stores across Arkansas with $12.6 million in sales. Had Sears’ leadership even noticed it, this small retailer was too small and insignificant to fit into Sears’ need for growth. The entrepreneur built on his experience running a chain of five-and-dime stores in rural Arkansas to iterate a new business model that could dominate an emerging market. He wasn’t building mall-based stores; his new store model was large enough to be the mall in rural markets like Bentonville.
By the time the Sears Tower was complete, Wal-Mart had gone public and was traded on the New York Stock Exchange. When Sam Walton stepped down as CEO in 1988, Wal-Mart had 1,198 stores with sales of $15.9 billion and 200,000 associates.
Sears’ fortunes were decimated by missing an important inflection point in retailing. Sears began moving its offices out of the Sears Tower in 1992. Harvard Business School Professor Clayton Christensen pondered how a company like Sears with such overwhelming advantages could be overtaken by an upstart like Wal-Mart. He knew that many of the Sears executives had among the finest business training in the world because they had been educated at Harvard. What could be missing in their Harvard Business education?
Christensen’s answer is Disruption Theory, which he popularized in his best-selling book, “The Innovator’s Dilemma.” Trained to make data-driven decisions, Harvard-educated executives of market leaders follow their data to improve their products targeted at their best, most profitable customers. An upstart that tries to get between a market leader and its best customers is likely to get annihilated.
A market leader like Sears is exposed to competitors like Sam Walton where they have the least information – the less profitable, low end of the market. Sears’ demise was not a failure of execution. Their management came to work each day doing what they had done for years, closely following their data to create a dominant, operationally excellent company. Sears’ demise was a strategy failure. Sears’ management never saw the market disruption coming. By the time they had the data to recognize the threat, Sears was toast. Wal-Mart was a significant company that had established a low-cost position in the market, and it was too late for Sears to change their higher-cost, mall-based strategy.
Can we use Disruption Theory to predict winners? Thomas Thurston is sure we can. Thurston is a partner at WR Hambrecht + Co, a Silicon Valley venture capital firm, and CEO of Growth Science, a data science prediction lab that uses algorithms to forecast the rise and fall of businesses. He observes, “Disruption Theory is the foundation of the most accurate, thoroughly vetted, quantitative prediction model of new business survival or failure in the world today.” That is a strong statement.
Only around 25 percent of venture capital-backed businesses survive. While previously at Intel, Thurston analyzed their investment portfolio to see if Disruption Theory analysis could pick winners better than the venture capital industry. He found that Disruption Theory blindly predicted if new businesses would survive or fail with 94 percent accuracy and over 99 percent statistical confidence. Thurston’s response: “Holy crap!”
Alan Kay said that “the best way to predict the future is to create it.” Disruption Theory is a powerful, predictive tool entrepreneurs can use to create the future.