A Dynamic Approach to Valuing Online Content

We are on the precipice of a revolution—a content revolution. Now more than ever, we are close to a world where the fair market value of information can be unlocked and put at the forefront, a world where freedom reigns and consumers can access the quality content they want, when they want it. Amazon has done it for physical goods, Spotify for music, Hulu and Netflix for video. It’s content’s turn. The online information ecosystem is the largest and most active market in the world. It touches our lives on a daily basis and it’s singular in its liquidity, yet no one has found an accurate way to price and model this market in a sophisticated manner. There’s a missed opportunity to capitalize on impactful, well-written content and what’s more to instil an appreciation of value in consumers who expect online content to be “free.”

The Right Approach

In order to posit dynamic valuation as a way for publishers to address these challenges, first we must discern whether unscheduled news can carry an intrinsic value. Unscheduled news (for example, Company X filing for bankruptcy) is distinct from scheduled news (such as IBM’s 8:30am Earnings Release) in that it is disseminated at a non-routine time. A poignant example of unscheduled news’ ability to influence economic markets is a fallacious 2008 Tweet from the APs hacked Twitter account: “Breaking: Two Explosions in the White House and Barack Obama is Injured”. Following this spurious Tweet, the S&P dropped by 14 points, the equivalent of $136 billion. The power of breaking news (even when it is untrue) to influence our world culturally and financially is succinctly demonstrated in this example.

It is key to develop a system of valuation that not only takes into account the natural decay or half life inherent to news media, but also responds dynamically to shifting environmental drivers and user consumption patterns. Price should also be predicated on a diverse array of contributing editorial and environmental factors and shaped in real time by the abovementioned consumption patterns. What’s needed is a self-learning, adaptive system that increases in richness, accuracy, and scope as more and more content it input, analyzed, bought and sold. Dynamically shifting price will increase both interest and motivation to buy, according to good, better, best, pricing ideologies and prospect theory’s loss aversion or “fear of missing out.”

 

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