An excellent and easy to understand example of this strategy is cited by Davenport and Harris in their book, Competing on Analytics.
The cited case study is Netflix. The very nature of Netflix’s business requires them to track customer rental habits and it should come as no surprise that they use that data to drive business decisions. One aspect of their business is distribution rights for DVDs.
A very basic decision in this part of their business is the appropriate volume of DVDs to purchase in order to distribute a given film. If they order too many copies of a film with very little interest among their customer base, the distribution obviously will not be profitable. If they do not order enough, they will not be able to meet the demand, fracturing their sales model, which is to say customers will stop subscribing. Netflix promises their customers they will receive their DVD order in about one business day and there is no contract; cancel at anytime.
Given their business model, it is critical that they use analytics to optimize their distribution decisions. In this example the company bought the rights to Favela Rising, which is a movie about musicians living in the slums of Rio de Janeiro, Brazil.
Because they pay attention to analytics, Executives knew that roughly one million customers had ordered the 2003 movie, City of God, which is a drama is also set in the slums of Rio. They also knew that about 500,000 customers rented a similar documentary about slum-life in India called Born into Brothels, and that 250,000 customers ordered both films. Executives therefore reasoned that an order for 250,000 was a sound decision.
This is an example of how analytics, which track customer behavior, can be used to minimize investment and maximize return. As important as it is, this is only one of many aspects of your business. Financial, R&D and even human resources analytics are also critical to making sound business decisions. One aspect of your business that you might have overlooked in the context of analytical assessment is your patent information and that of your competitors.
The system that Luhn described also embraced the concept of collaboration. It is not enough to simply ask these questions and to probe the situation to make sure they are thoroughly answered. You need systems to ensure that the information discovered regarding what is known and who knows what are delivered to those who need to know. Patent Management plays a crucial role in this. It is important to note, though, that care must be taken when trying to understand the who in who needs to know.
Some data may seem more applicable to one or just a few members of the team, and as a result, data sharing may occur only among those team members rather than the entire team, a fact that can unnecessarily limit your analytics. Just because it might not seem applicable to everyone, doesn’t mean that the larger research effort will not be served by giving everyone access to it.