[Part 1 of an assignment for INFS 762]
If Netflix did nothing more with IT than process online movie orders, they would likely still have gained significant competitive advantage against Blockbuster, Mr. Movies, and other brick-and-mortar movie vendors. I can go online, select from a 100,000+ DVD library that offers more variety than any physical store can, and get what I want by mail in two days. (I could also watch over 12,000 of those videos instantly online… if I had a slightly faster Internet connection!) They charge no late fees, a move that drove Blockbuster to ditch most late fees in 2005 and lose $400 million (Mullaney, 2006).
But Netflix has also made good use of data mining to enhance its competitive advantage. Its Cinematch recommendation engine analyzes customer rental patterns and movie ratings to help the company recommend new rentals. The system also helps Netflix make smart investments in a wider range of films. Mullaney (2006) offers one simple example: Netflix used rental patterns of the film City of God, set in Rio, and the documentary Born into Brothels to predict expected rentals and determine a reasonable fee to pay for DVD rights to Favela Rising, a documentary about musicians in Rio. Mullaney points out this sort of analysis opens the door for more independent filmmakers, as Netflix can identify more niche film markets and expand distribution for smaller-budget films without spending too much. Netflix is thus able to build its business model on “backlist” films comprising 70% of its rentals, compared to traditional video stores, where backlist films make up just 20% of rentals (Thompson, 2008). Increasing demand for lesser-known films reduces demand for big-studio blockbusters, which in turn saves Netflix money, as revenue-sharing agreements with the big studios take a bigger bite out of Netflix’s take (O’Brien, 2002).
Netflix has also been able to discover connections in movie preferences to guide its movie recommendations, from seemingly obvious overlap between customers who like The Patriot and Pearl Harbor to more curious associations between affinity between rentals The Patriot and Pay It Forward and I, Robot (Thompson, 2008). Netflix considers its recommendation system crucial to its business. The company didn’t have any such system when it opened in 1997 and didn’t feel it needed one. But as the library expanded beyond the original 1000-title collection, Netflix realized customers needed help to find films they would like. “‘I think that once you get beyond 1,000 choices, a recommendation system becomes critical,’ [said Reed] Hastings, the Netflix C.E.O…. ‘People have limited cognitive time they want to spend on picking a movie’” (Thompson, 2008).
The recommendation system also keeps people subscribing and buying movies. Cinematch provides sufficiently valuable results that in October 2006, when Netflix found it was having difficulty improving he performance of its data-mining algorithms, it announced the Netflix Prize: $1 million for the first developer who could improve the system’s performance by 10% (Thompson, 2008). The contest drew over 44,000 submissions, including a flurry of submissions during a one-month contest-ending race triggered by contest rules at the end of June, 2009, when the first team reached the 10% threshold (Lohr, 2009). Teams were able to achieve significant gains through mathematical algorithms like singular value decomposition (Thompson, 2008). And Netflix was able to take advantage of the collective inventiveness of nearly 5,000 participants to improve its data-mining algorithms for a price tag that might have covered the full-time salaries of eight entry-level developers over the same time period.
There are still quirks of human behavior that defy complete explanation of movie preferences by data-mining methods. However, as Mullaney (2006) puts it, Cinematch is able to take decisions that used to be based on gut feelings about the appeal of various films to various audiences and put them on a stronger footing of better and actual patterns of customer behavior.
Lohr, S. (2009c, July 28). Netflix Competitors Learn the Power of Teamwork. The New York Times. Retrieved July 30, 2009, from http://www.nytimes.com/2009/07/28/technology/internet/28netflix.html?_r=1
Mullaney, T. J. (2006, May 25). Netflix: The Mail-Order Movie House That Clobbered Blockbuster. BusinessWeek: Small Business. Retrieved July 30, 2009, from http://www.businessweek.com/smallbiz/content/may2006/sb20060525_268860.htm
O’Brien, J. M. (2002, December). The Netflix Effect. Wired, 10(12). Retrieved August 2, 2009, from http://www.wired.com/wired/archive/10.12/netflix_pr.html
Thompson, C. (2008, November 23). If You Liked This, You’re Sure to Love That. The New York Times. Retrieved August 2, 2009, from http://www.nytimes.com/2008/11/23/magazine/23Netflix-t.html