The Challenge of Content Management

A large number of online media companies source their content from the traditional content-producing media companies like news agencies, recording labels etc. Consequently, handling content and presenting it in a particular common format is a major challenge for these companies especially when content is sourced from several providers with differing presentation patterns. Content management at this level involves two broad tasks:

• Mapping all content to a common format for presentation and for ease of mining.
• Categorization of content by user intent.

Storing content in a common schema is essential if content mining is to be automated to some extent. However, the bigger challenge surfaces after this when the content is to be categorized. Categorization is normally done to aid the user in browsing the content and also in presenting relevant search results.

Categorization can be done based on several parameters. In case of music, the problem is simplified to a great extent since most of music is categorized by genre and further by artists where an artist can fall under several genres. In this case, the categorization can be fully automated once the taxonomy of genres and artists is in place. However, in case of language content which needs to be categorized by subject matter, a good deal of social intelligence is required to understand the topic to which a certain item belongs. In such cases, machine learning and classification will always yield errors. Errors may dip with increased machine learning but will always remain. Hence, some amount of manual effort is essential in such categorization. The trick then is to figure out which parts should be automated and which parts manual since there is a trade off between cost and accuracy here.

Categorization essentially can be broken down into two further tasks:

• Finding categories ‘related’ to an item.
• Deciding which of those categories is/are actually ‘relevant’.

In the case of every item being mapped to only one category (one-to-one mapping), the second problem can essentially be combined with the first. However, in a one-to-many mapping, one needs to use social intelligence (possible with a manual workforce) to determine which categories are relevant.

To provide a general rule (with exceptions), finding related categories should be automated, especially in a one-to-many mapping since an algorithm will do a more exhaustive job than a human. However, in deciding which of these categories are relevant, manual workforce alone can bring in the level of social intelligence that is required to determine the subject matter of a certain item and accordingly categorize it.

Dogster: Thinking outside the Kennel!

I stumbled upon Dogster on an idle afternoon while trying Google’s search results for some weird queries. Just when you thought you’d seen it all, you land up at a social networking site for dogs! Or, to be technically correct, for Dog Lovers! The very idea seems incredulous and you might wonder who’d be goofy enough to pose as his own dog and write blogs form his dog’s perspective.

That is, until you realize that it is actually a neat business idea to target and serve a distinct segment of the consumer population. By catering to a niche segment, this provides the $36 bn pet industry advertisers with highly relevant eyeballs. And the results speak for themselves.

Dogster and Catster are community sites for owners to share pictures and diaries of their pets. The founders of Dogster Inc apparently started up the sites as a parody of Friendster, and the like. Dogster's advertising rate at around $5 cpm is almost 40 times that of MySpace, a much more general SN. Dogster makes 95 % of its revenue from advertising and sponsorships and the rest from premium subscriptions. Advertisers include names as big as Disney, Target, PetSmart, Gap and Warner Brothers.

What makes a seemingly crazy idea like Dogster click? Dogster creates a new user experience since it orients itself around dogs rather than actual people. Users can stay anonumous and yet identify with something as dear as their pet. Since users share a common interest, canine related nomenclature and puns are plenty. People love to talk about stuff they are really passionate about.

The site is doing great with more than 300000 registered users. CEO Ted Rheingold followed up Dogster’s success with launching Catster and now wants to extend this to cover every pet and ultimately every hobby.

Netflix: Success of Simplicity

Netflix is a potent example of simplicity working wonders. It operates on a very simple business model allowing subscription for home delivery of DVD movies. Why does it work? Primarily because it is an answer to the single biggest hassle to DVD rentals: sky-rocketing fines on returning the DVD late. Providing various schemes depending on period of subscription and number of rentals during that period, it allows the users to retain the DVDs as long as he wants to without worrying about late fines. The favorable consumer experience is extended by allowing online ordering and pre-paid return envelopes for the DVDs. Additionally, Netflix also mines user behavior to recommend movies to the user based on his previous choices.

The flexibility, ease of use and comprehensiveness of choices with Netflix have resulted in a faux ownership for the users. Any system which relaxes constraints on the user experience is going to achieve greater acceptance. Beyond this, a few eternal factors have also helped the company make hay. Sales of DVD players took off around the time Netflix switched to a subscription service in late 2000, and Hollywood studios signed deals with the company around that time to counter the growing power of Blockbuster.

Netflix’s business model has redefined the DVD rental industry in such a big way that retail giant Wal Mart has backward integrated to incorporate such a system and long time competitor Blockbuster has also worked towards a similar model. Back home in India, www.seventymm.com has worked on a similar model with some success.