A Better Business Model From Netflix

By By Domenic Venuto

It wasn’t long ago that publishers generated advertising and subscription revenue by aggregating viewers to monetize content. Viewer attention was abundant when the number of magazines and content was limited. Today, content is abundant and user attention scarce. Publishers struggling to work out how to monetize that scarcity should look to the subscription charging Netflix as a model of best practice.

Netflix’s term-based subscription model for movies had as many doubters as publishers have today for charging online subscription fees. With the advent of the DVD, home video consumption had shifted from rental to purchase. Most believed a subscription model would was no longer viable. Blockbuster had a near monopoly on the offline rental market and their financial performance had begun an increasingly rapid decline. Online music subscriptions services had all failed.

Midway through Netflix’s 10th year, they remain a beacon of long-term success amongst online media companies. Over the past 5 years, their stock has increased 336% while Blockbuster’s stock has decreased 69%. DVD sales have begun to decline. Potential competitors, including start-ups, Wal-Mart, Blockbuster and movie studio joint ventures have been unable to capture a significant market share.

Netflix did not just distribute content facilitated by an online search and queue management tool. They created a new business model built on consumer insight and data collected from their online activity. Content, engagement, pricing strategies and revenue stream were woven together in a compelling offering only made possible by the power of implicit and explicit data.

Implicit data leverages the web’s ability to collect information on where a user clicks – content or ads, searches performed and content consumed (or not consumed). This alone isn’t a competitive advantage that would create a defensible business model because it often fails to ascertain a user’s true intentions.

Expanding to explicit data collection resolved these limitations. It leveraged the web’s ability to enable users to contribute information about themselves, their friends, content they liked, items they purchased and things there were interested in purchasing. Netflix has pioneered eliciting explicit information from users and they didn’t do it by tricking customers into providing useful information. Through emails and throughout the website Netflix clearly explains how members can receive faster delivery from creating queue lists and better recommendations from movie rating submissions.

The company also expanded into collaborative data collection. Collaborative data allows users to interact and share their implicit and explicit data by following each other’s content ratings and comments. Netflix enabled members to create a friends list and share their own movie ideas and information with their friends. They recently expanded this capability by integrating Facebook Connect. Members can opt in to have their Netflix movie rating posted on Facebook. Friends will be able to make comments about the movie and click a link to the film’s page on Netflix.

The results of this data-based business model, has led to increased consumption and improved recommendation accuracy. Netflix members say they rent twice as many movies per month as they did prior to joining the service. Approximately 60% of subscriber movie selections are Netflix recommendations based on implicit and explicit data about the user. More than 90% of Netflix members say they are so satisfied with the Netflix service that they recommend the service to family and friends.

Imagine publishers getting these types of results!

Netflix’s recommendation conversion rate is 200 times the current 0.1% -0.3% banner click through rates. The meager performance of hyper-targeted ads on media sites should not be attributed to hyper-targeting itself, but their execution of it. Significantly higher conversion rates would increase CPMs. For example, new mobile text networks provide ad inventory that is hyper-targeted by cross referencing data submitted by the user when opting in for text content with Experian credit data. The response rates range from 5% to 15% and the CPM rates range from $75 to $100 for the low engagement ads. Although the Wall Street Journal online has been the focus of discussions on the viability of the subscription model, the content and pay wall have ironically provided a homogenous audience that emulates improved targeting. The CPMs at the Wall Street Journal have consistently been 6 to 7 times its competitors and their high engagement online video ads have achieved $90 CPMs.

In an economy of doing more with less, publishers should be building engines capable of extracting and aggregating valuable data from their user base and using it as fuel to increase revenues.

The author gratefully acknowledges Paul Gelb, National Manager, Emerging Media, with the New York City office of Razorfish, who contributed to this byliner.

Minsiders columnist Domenic Venuto is SVP and Head of the Media and Entertainment Practice with the New York City office of Razorfish. He can be reached at domenic.venuto@razorfish.com.

The author gratefully acknowledges Paul Gelb, National Manager, Emerging Media, with the New York City office of Razorfish, who contributed to this byliner.