Personalization is one of the great un-kept promises of digital media. From the time I started covering online publishing in 1995, content providers have been playing with the model with few examples
of sustained success. This is as much a user issue as it is a publisher problem. Many of us like the idea of a more streamlined content experience that aggregates large volumes of news around our
particular interests, but very few of us want to put effort into creating the necessary input (registration, profile building, or even simple box ticking) it takes to get some level of
personalization. I came to think of this as the 10% problem. Over many years of interviewing scores of publishers on the topic, I kept getting the same answer to the same question. How much of your
user base is electing to personalize the experience? Ten percent or lower was the standard response.
Which is not to say that personalized services have not thrived, but the success cases
occur almost always outside of the realm of news and information. Amazon has hands-down the best personalized service online. It leverages the usage patterns of you and other buyers to create the
closest thing to a concierge experience we have online. Netflix, while not as sophisticated as Amazon, effectively pushes relevant recommendations to DVD renters in a way that uncovers unknown gems
and forgotten must-sees. But also in this mix is Pandora, which shapes Web radio stations by matching the characteristics of a track against your own declared taste and recent responses to previous
tracks.
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Amazon, Pandora, and Netflix have a simple personalization principle in common, says Eduardo Hauser, CEO of DailyMe: "They require very little effort or input but give a lot of
output." The value delivered is wildly disproportionate to the effort required. Traditionally, this has been the missing element when it comes to personalizing news: real interest.
Hauser
discovered this firsthand. DailyMe launched in July as a personalized aggregation of newspaper and wire content across all categories. DailyMe licenses content from 500 publishers to process 20,000
stories a day. Users registered with the service and declared their preferences in order to create a tab of custom headlines on the front page. But as Hauser admits, "performance has been less than we
would like" -- about 100,000 uniques a month and 25,000 who registered and formed profiles.
The original model was based on "strict filtering," whereby a user asks for content type X and
gets content type X. Even at this rudimentary level of personalization, however, DailyMe illustrated the clear benefits of personalization when it comes to engagement. "We discovered that people who
went through the process of creating profiles are pretty good users. They will look at seven pages per visit and spend 45 minutes a month and come back three times a week." The problem was scale. "The
number of people willing to give you that much information didn't scale very well," he says. "We have to do it differently."
While Amazon, Pandora and Netflix suggested a general principle for
making personalization work (little input, big output), news content is different in kind from music, book or DVD shopping. First, news has an issue of "maturity" some of these other content types
don't have. "News cannot be re-recommended until you buy it," says Hauser. Moreover, a personalization engine for real-time aggregated content never knows exactly what content will be in its inventory
at any given time. And finally, news is consumed differently from these other media types. People flit across providers, and it is inherently difficult to get the user to invest time even to register
with a site, let alone personalize it.
"The way to fix this is to engage in dynamic personalization," says Hauser. "We are going to deploy a system that will require very little input and
give a lot of output, be suitable for news, learn from the user, and not require registrations."
Scheduled to launch in May, the next iteration of DailyMe will use a cookie-based approach to
tracking incoming users ties to their email login. The engine will map a user's history onto a histogram or frequency distribution chart. The content will be cataloged from tagging at the source as
well as DailyMe's own engine that will map 141 categories. A number of things go into the content analysis, from examining the number of images in a piece or even characters involved to determine its
depth. A rank of sources will try to determine which of two similar pieces is coming from a more authoritative publisher on that particular topic.
The user's histogram will be populated by a
series of data points that go far beyond the content types a user views. Hauser says that elements like time spent in categories, entry and exit pages, etc. will help profile the user's level of
interest. Whether the user emailed or voted on a story exposes his level of engagement with the topic as well. "All of these data points are first normalized against the entire audience and then
divided into time spans to distinguish between short terms of 3 to 7 days or longer term interest," says Hauser. An algorithm will make predictions about news likely to be of interest to particular
users. The system will apply machine learning technologies from computer science to the task of building a profile on that user in background.
On the business side, what becomes interesting
about this news personalization engine is that it can also serve as an ad personalization engine. The same data about usage, interests, etc., that is passed to the content engine for determining the
editorial mix could also be passed on to the ad network.
Of course, creating a more passive personalization model that responds to behavior rather than profiling is only part of the larger
problem. News is commoditized online and most of us either use a search engine or multiple sources. Expecting anyone to invest large amounts of time with any one general news source is outmoded. And
so a secondary realization for DailyMe is that it had to become a technology for licensing to others and not rely on a destination model. The new cookie-based technology makes it possible now for the
engine to be licensed out to publishing partners.
There has been a lot of discussion lately about how over-personalizing the media experience creates narrow ruts of interest and knowledge for
us and robs us of serendipity and discovery. This is true, and Hauser is keeping that prospect in mind by looking for ways to make DailyMe a blended experience, an editorially driven home page and
perhaps behaviorally driven article pages. Personalized news could occupy a box amid a standard news home page, in much the same way Forbes.com has its Attache customized news bar on the side of its
site. In some sense, over-using past behaviors to predict future behaviors can actually undermine the users' attempts to move their news gathering habits online. Personalization without the
opportunity for greater discovery over-solves one problem and helps create another.