Leadership & Management

Netflix Broke the Rules and Won

Research explores how the DVD rental company succeeded in a tough marketplace.

February 01, 2010

| by Bill Snyder

The shifting consumer market for video entertainment may be causing sweeping changes in the video rental market but Netflix has still left an indelible mark with its revolutionary patented business model that includes open-ended rentals for a fixed fee.

Rather than the conventional model where customers go to a store and pick out the movie to rent, in 2003 Netflix was granted a patent on a model that allows customers to pay a fixed monthly fee, create an online wish list for future rentals, and then order them over the internet. Once they arrive, customers can keep the videos as long as they like with no late fee. The model was so revolutionary that in 2006 Netflix sued competitor Blockbuster for patent infringement. It was settled out of court in 2007, with the terms kept under wraps.

Although the Netflix patent contains a good deal of detail about the video rental business, it is also quite broad, since it applies to “a method for renting items to customers” and “computer-implemented steps” — either of which could extend to online rental of nonentertainment items.

The novelty of the model and the possibility that it might be applicable to other businesses intrigued Sunil Kumar, the Fred H. Merrill Professor of Operations, Information, and Technology at Stanford GSB, whose research focuses on analyzing mathematical models of operations, particularly congestion phenomena.

Along with colleagues Achal Bassamboo of the Kellogg School of Management, Northwestern University, and Ramandeep Singh Randhawa of the Marshall School of Business, UCLA, he studied the Netflix model with the goal of determining what effect the lack of deadlines has on customers. The researchers also wanted to know the smallest amount of inventory Netflix could carry while still having enough movies on hand to satisfy customers.

The answers were straightforward: “Netflix got it right by not imposing deadlines,” says Kumar. And the company needs to stock only a small fraction of the total demand for any one video and still provide good service. But the method for calculating those answers was fairly complex. Indeed, nearly all of the 23-page working paper, “Dynamics of new product introduction in closed rental systems,” is mathematical proof of those seemingly simple conclusions.

Building a model to represent Netflix’s business was difficult because of the enormous number of transactions that occur every day. Rather than trying to capture so many events, the researchers used an analogy from engineering — the behavior of fluids. “Imagine that Netflix is a huge reservoir that drains and refills as people rent and return movies,” explains Kumar. “We need to know how a system works with many users, and the alternative (to the fluid model), tracking individual users, is hopeless. The fluid model is a usable approximation that works well as the number of users gets large.” The fluid metaphor exists in classical statistics. Proving that it applies in this business case is a contribution of the paper, he notes.

The Netflix business model contains an interesting set of tradeoffs. When a customer keeps a movie for an extended period of time, Netflix is deprived of its use, which is a negative. Deadlines help ensure prompt return and thus have the benefit of reducing the number of copies that Netflix needs to stock to provide good service. But because the customer can’t rent another movie until it is returned (this is known as “max out” and is included in the patent), deadlines speed up demand for other movies and trigger costs of fulfillment, such as postage, which Netflix pays. Kumar and his co-workers show that there is no tradeoff here at all — not having deadlines is a win-win.

It’s important to note that Netflix isn’t flying blind. Customer wish lists give the company real insight into what movies its customers want. In theory, Netflix could simply stock one movie for every customer who wants to see it. But that, of course, would be a terrible business practice. So the question remains: How many copies of a given movie should the company stock, and is it really better to eschew return deadlines?

The researchers realized that customer behavior varies. In fact, it’s pretty random. Some people hold movies for a week, others for two or three weeks. And the behavior of each individual customer varies from time to time. The timing of new movie releases is also random. It turns out that those two facts are key to resolving the questions. To demonstrate why, Kumar uses an analogy familiar to many business students, “the inspection paradox.” Here’s how it works:

Suppose we note that taxicabs pass a given corner on the average of 1 every 10 minutes. Then assume I show up at a random time, and I’m told that a taxi left the corner 8 minutes ago. At first glance, one would assume the next taxi would arrive in 2 minutes, on average. But that’s incorrect; the real answer is much longer, and depends on the degree of randomness. That’s because the 10-minute interval is an average, so if a customer shows up at a random time, she is less likely to hit a shorter interval and more likely to hit a longer interval between taxis.

The release of a new movie is like the appearance of the taxi, and the customer’s readiness to rent another DVD is analogous to showing up at the corner. The imposition of deadlines would remove some of the randomness from the equation and push customers to rent faster, resulting in higher costs. Removing deadlines creates a better balance of supply and demand, the researchers conclude. In fact, under the ideal conditions established in Kumar’s model, Netflix would only have to stock a small fraction of any given movie to satisfy customers and control its costs.

Because the model built by Kumar and his colleagues is just a model, and not reality, it would be useful to compare the results of the researchers’ simulation to the company’s actual results. But Netflix did not provide the data, citing costs of data collection and data cleaning, says Kumar.

Even so, the results of the paper raise an interesting question: If the no-deadline model works for Netflix, would it work for another type of business? The answer is maybe, says Kumar. For example, it would not work for a car rental business, since people tend to rent a car and return it without renting another. But consider the customers of an equipment rental business. One might rent, say, an air compressor, at the beginning of a job, and then return it and rent a backhoe, suitable for a later stage of the job. And that could mean that a no-deadline policy would work. “It’s worth examining,” says Kumar.

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