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Analytics and Strategic Thinking

Perhaps the trendiest business idea these days is "business intelligence," also known as analytics. It has become prominent in the hypercompetitive world of sports -- popularized in Michael Lewis’s book, Moneyball.

Analytics were also prominent in President Obama’s reelection, where Time and other news sources reported how the Obama campaign team used “big data” to effectively target its campaign spending, and to accurately track the campaign's effectiveness in key swing states. As Michael Scherer of Time concluded in his November 2012 article, “The time of guys sitting in a back room smoking cigars, saying ‘We always buy 60 Minutes’ is over. In politics, the era of big data (operated by quants and computer coders) has arrived.”1

The analytics movement has also permeated business. The Obama campaign’s “chief scientist,” who put together an analytics department five times as large as the one used in Obama’s 2008 campaign, had previously cut his teeth crunching huge data sets in retail, learning how to “maximize the efficiency of supermarket sales promotions.” More and more, senior executive staffs are including "quant" people (quantitative analysts) who not only understand analytics but can do some of the crunching themselves.

For example, the Memphis Grizzlies of the National Basketball Association hired John Hollinger. Described as “the most famous stats-minded basketball writer in the world,” Hollinger is now VP of basketball operations -- heady stuff for a journalist/quant geek.2 Over the last 10-15 years, large companies have done this as well.

There is no doubt that analytics can be important in helping firms compete and win. Analytics provides real-time information that informs decision-makers. Thomas Davenport claims in his book, Competing on Analytics, that analytics, when done well, not only leads firms to greater cost efficiencies but is a tool for better decision-making. Analytics proponents offer it as an antidote to decisions based on gut or intuition. For example, for years Harrah’s Casinos operated on the assumption that their most profitable customers were the big stakes players (called whales). While these players did create a lot of revenue, they were not as profitable as elderly slot machine players, and this information helped Harrah's adjust tactics to cater more to senior customers.

Analytics can help overcome managers’ tendency towards action over reflective learning. That is, managers often interpret signals and anecdotal artifacts hastily and incorrectly, whereas effective analytics can provide empirical analysis that better informs decision-making -- and because of advancing technology and processes, do it in real time.

An effective analytics program would involve the following:

  • Access to data, preferably in real time. Well-defined markets lend themselves to having lots of data throughout the value chain -- from raw materials monitoring all the way to customer behaviors. In a business that is mature or maturing, the ability to transform this data into useful information (the real utility of analytics/business intelligence) can be essential to being able to compete.

  • Identified key metrics. This requires an understanding of the key drivers for the business and the firm’s specific strategic imperatives. That is, analytics should focus on important things to bring useful information to the decision-maker. For example, a franchiser owning 40 restaurants will find one of its key goals is customer loyalty. Analytic programs should look at customer data that measures satisfaction and loyalty. A case study on Concord Neighborhood Corporation (an Applebee's franchiser) depicts a robust analytics system that informed better decision-making and led to better results.

  • Data-crunching software sophisticated enough to do super-fast inquiries to sort through data. Technology has made this possible through IT capabilities. Furthermore, systems like Point of Sale (POS) offer chances for data to be real-time and thus more actionable.

  • Executive commitment and company-wide adoption. An analytics program should not be driven by IT or be isolated in one functional department -- it should be driven from the top. In the Concord restaurants example, analytics was driven by operations people, based on the information that was important; IT supported and designed systems according to the business’s needs. Harrah’s, under the leadership of Gary Loveman, is another example of a firm using data comprehensively throughout all parts of the business.3
The bottom line is that analytics can turn data, which is ubiquitous now, into useable and actionable information, and it can overcome the tendency to depend on gut instincts, intuition or anecdotal evidence as the basis for decisions -- especially tactical and operational ones.

What analytics does not do is eliminate the uncertainty that is always associated with strategic planning. As Roger Martin states in a recent Harvard Business Review blog post, “Contrary to popular opinion, strategy is not about turning uncertainty into certainty.”4 Not even a turbocharged analytics function, such as that employed by the Obama campaign, can provide proof for any proposed strategy or strategic plan.

Effective analytics can be immensely valuable in informing tactical and operational activities, but strategy is about planning for the future, and seeking profitable opportunities that others have not seen or acted upon. Next month, we will visit the keys to effective strategic thinking and an approach to seizing opportunities.

1Michael Scherer, “Inside the secret world of the data crunchers who helped Obama win,” Time, November 7, 2012.

2Eric Freeman, “The Memphis Grizzlies have hired statistical analyst John Hollinger of ESPN.com,” Yahoo Sports, December, 13, 2012.

3See article by Karl Greenfeld, “Loveman plays ‘purely empirical’ game as Harrah’s CEO,” Bloomberg, August 5, 2010. http://www.bloomberg.com/news/2010-08-06/loveman-plays-new-purely-empirical-game-as-harrah-s-ceo.html

4Roger Martin, “Placing strategic bets in the face of uncertainty,” HBR Blog Post, January 22, 2013.

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