Blazing the new digital trail

More companies than ever before are keeping track of what their customers are asking for and buying. How do the best companies take data ...

More companies than ever before are keeping track of what their customers are asking for and buying.BlazingthenewdigitaltrailHow do the best companies take data from millions of transactions and convert it to a successful business strategy? Larry Rosenberger, John Nash and Ann Graham have been exploring the new frontier of data, analytics, decisions and actions.

By 2010, businesses and consumers worldwide will spend $1.5 trillion annually on information technology hardware, software and services, according to the US information technology market research firm, IDC. Displacing the paper trail is a digital trail, residing in computer databases, both private and public. There isn’t a business or a consumer anywhere today that isn’t touched by the trillions upon trillions of revealing bytes moving through wired and wireless, stationary and mobile information technologies.

For all leaders of 21st-century corporations, the most urgent question is: how can companies create financial and customer value from unprecedented access to such huge amounts of digital data? But almost equal in importance is this question: can companies also use this data to create business value?

In business-to-consumer industries, analytics (the science and art of identifying and interpreting patterns in data, then technologically massaging that data to accomplish a defined task) are all the rage. Some companies have mastered analytics. Tesco, the UK-based global grocery and general merchandise retailer, and Netflix, the US online DVD-rental service, have both used proprietary algorithms coupled with customer transaction databases to create cost-effective, mass-customized consumer experience business models in their respective markets. Such companies have not only been successful in their own right, they have often disrupted the success of their competitors.

Busting open

When Reed Hastings founded Netflix in 1999, he saw video stores as a prime target for disruption; and he saw Blockbuster, then the market leader, as the one to beat. Blockbuster, which launched its online channel in 2004, hasn’t been able to grow its customer base even within striking distance of Netflix. In 2008, Netflix claimed 7.5 million customers, while estimates are that Blockbuster has perhaps half that many.

Nor has Blockbuster been able to beat its rival on the quality of the experience Netflix delivers to its customers, largely based on the strength of its mathematical skills and business acumen. The way Netflix outperforms Blockbuster most is through its recommendation engine, called Cinematch, which runs proprietary predictive algorithms that analyse individual customer buying patterns and opinion ratings of movies to predict which movies the person will like. The algorithm is written to optimize both a customer’s preferences and inventory conditions. It can even pick up a customer’s tastes for obscure and cult movies.

While Blockbuster rewards its regular customers with traditional volume discounts (that is, if you order more movies, you’ll pay less for each one), Netflix rewards its regular customers by getting better at knowing what they prefer with every transaction and giving them choices they will like every time they reorder. The more a Netflix member uses its services and the recommendation tools on the sites, the better the algorithms get, the more personalized and appealing the experience becomes over time and the more valuable the customer data becomes.

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