Part 4 of a multi-part series concluding 9 days from now, more or less
Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 5.5 | Part 6
Here is the fundamental business model of Netflix: a user pays a monthly subscription fee to have DVDs mailed to them, the number of which is limited only by the swiftness of the US Postal Service (and available free time for video watching). The user watches the DVDs and mails them back in order to get more. There are no late fees; the sooner you return your movies, the more movies you are able to receive. The company makes money by selling more subscriptions. It’s pretty simple.
So, how do “better” recommendations translate into better business success for Netflix? As we have pointed out previously, judging whether a movie recommendation has been successful can be a subjective enterprise. The ultimate measure of success is not accuracy in predicting ratings but, rather, customer satisfaction which promotes increased usage and increased advocacy for the service. Learning this simple rule (”successful recommendations drive successful business”1) has led to important conclusions for recommendation technology providers like MediaUnbound. For example, each implementation of recommendations is unique and requires different types of technology, tuning and features. Recommendation science is not a theoretical exercise; it only makes sense as an applied and practical problem. The best holistic approach to judge the quality and accuracy of a recommendation system is to evaluate it against a service’s key business performance metrics.
In the case of the Netflix Prize, we first need to outline the business performance metrics before determining whether the strictures of the contest will actually lead to a 10% improvement. For Netflix we have:
- Reducing churn rate. Netflix doesn’t want current customers to cancel their accounts.
- Upgrading to higher-value plans. Netflix wants current subscribers to upgrade to more expensive plans.
- Enticing new users to sign up. Netflix wants to acquire more subscribers.
The goal in spending $1M on the contest should be to improve user experience and positively affect one of the three metrics. But, what is the link between “better” recommendations and each metric?
A concrete example is useful here. Returning to our ancient friends from earlier episodes, imagine the Greek movie rental subscription service netSkix.2 netSkix has exactly one current subscriber: Crito. In an attempt to turn netSkix into the most successful movie rental service in all of Athens, netSkix has hired Socrates–the most rational, knowledgeable (and doggedly persistent) movie seer of his time, to make movie recommendations for a salary of one million drachma. How can Socrates justify his cost through providing movie recommendation services?
Churn rate: In a subscription environment, the viewer is paralyzed by an abundance of choice. Almost any piece of long-form visual entertainment imagineable can be added to the queue.3 A good recommendation system will help the viewer navigate this choice–always feeling that there are a large number of interesting items to watch but never feeling overwhelmed by the choice. Socrates’s first challenge occurs early on his very first day of work:
Crito: “Socrates, there are no more movies to watch. I have watched every single Will Ferrell movie. Having completed my education in movies, I think I shall quit netSkix.”
Socrates: “So you have seen even A Bucket of Blood (1995) featuring Will Ferrell, the bad Roger Corman remake of the already bad A Bucket of Blood (1959) also by Roger Corman?”
Crito: “Don’t play the fool. That’s not available on DVD, Socrates.”
Socrates: “Oh, oops. Well, since you like Will Ferrell so much, have you thought to watch movies of other SNL comedians in their prime? Like, Stripes with Bill Murray?”
Crito: “I know Bill Murray. He’s that disaffected businessman guy who is in all the movies with Will Ferrell’s friend Owen Wilson. I guess I will watch this Stripes movie and then afterward I can quit netSkix.”
Plan Upgrades: Once Socrates entices Crito to keep his netSkix subscription (theoretically, as long as Socrates offers new good movies for Crito to watch, Crito will remain with netSkix), the next challenge is to convince him that he needs the top level of service. This upgrade will cost money but it will also open up better features and more freedom within the service, like getting more movies at any one time.
Socrates: “Crito, since you enjoy the movies of SNL cast members, I think you might want to also watch all of Dana Carvey’s work.”
Crito: “There are only three Dana Carvey movies.”
Socrates: “You must be forgetting Clean Slate and his short-lived sketch comedy show.”
Crito: “OMG! How will I be able to watch all these great movies. I will definitely need to upgrade my subscription so I can watch more movies every day.”
New User Subscriptions: Finally, to earn his weight in drachmas, Socrates should be able satisfy Crito to such an extent that Crito will recommend the service to all of his friends, resulting in more subscriptions for netSkix. This is one of the most powerful ways that netSkix will grow beyond its proud but paltry customer base of one. Glaucon, friend of Crito but not a netSkix user, seeks out Socrates who is only a couple of days into his job:
Glaucon: “Socrates, I was truly impressed by the film Down By Law that you recommended to Crito. It combined a good jailbreak movie with high comedy. We watched it during our weekly movie night.”
Socrates: “Did Crito enjoy the film? It seemed like a good intersection of his interests, though I warned him it might be out of his comfort zone.”
Glaucon: “Though I am not sure he thought that Roberto Benigni was as funny as Will Ferrell, Crito liked the film, nonetheless. More to the point: I was hoping I could subscribe to netSkix in order to enjoy your recommendations further.”
All three of these examples hinge on Socrates’s ability to build trust with the user, Crito. While it is important for Socrates to deliver good quality recommendations, it is more important not to deliver bad recommendations. Bad recommendations severely undermine the trust between service and user, sometimes permanently. A history, even a short one, of decent recommendations sets the stage for more adventurous recommendations. Recommendations must be individually tuned, responsive to the shifting context of customer usage. A successful system will prolong the customer’s subscription and help them explore the service. It may even let them feel that they are expanding their tastes. Even though Socrates was far from certain that Crito would enjoy Down By Law, he still offered it to him in the hope that he would make Crito reach a little to broaden his palette. In this case, it worked. Even if it had not, Crito should be able to discern why Socrates had recommended the movie: it clearly contains elements of Crito’s movie preferences. The bad recommendations which come out of left field, with no discernible logic, are the ones which Socrates must avoid because they can nullify any previous good recommendations.
It ends up that Socrates can quantifiably benefit netSkix with some combination of the following: retaining Crito as a customer; getting him to upgrade his account; attracting new customers. Socrates does not succeed in these categories simply by his raw talent for determining user ratings. Instead, he embraces a whole suite of recommendation faculties to serve each customer, making them happy about being a netSkix customer and encouraging increased usage, engagement, and referrals.
Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 5.5 | Part 6
Footnotes
- Chris Anderson watch out, we too can boil down complex business concepts to pithy one-liners. [↩]
- The “skix” root of netSkix is a reference to the ancient Greek word skia which means “shadow,” yet itself a reference to Plato’s cave allegory wherein folks who were not yet of a higher consciousness watched the shadows of objects moving behind them which were illuminated by the light of a fire. You could call this the ancient Greek version of movies, albeit very primitive ones. The whole phonetic similarity to Netflix was too much to ignore. We beg your indulgence. [↩]
- Except, for safety purposes of course, The Entertainment. [↩]
Ive been researching this and I’ll have to agree