Monday, November 7, 2011

Pay as You Exit: FairPay Explores New Content Pricing Discovery Regimes

In my childhood, television didn't have much new and original programming, so local TV stations would recycle a lot of ancient movies, cartoons and serials from the early days of films - some silents, even. "Our Gang," also known as "The Little Rascals," was a regular comedy feature in those days for children's programs, featuring a motley crew of kids portraying the ups and downs of living in the Great Depression. One episode called "Pay as you Exit" saw the gang putting on a talent show with the novel idea of letting people come for free and then paying what they wanted to when they were exiting. Long story short, the show was an accidental success and the kids wound up with a hatful of money from their grateful patrons.

It seems strange in a way to think that such an idea might actually help to save today's premium content sellers from their often rigid pricing regimes that seem to hold back their growth potential, but Richard R. Reisman, President and founder of Teleshuttle Corporation, is betting on just such an idea having merit for today's electronic content markets. Richard's solution is a new system called FairPay, a pricing exploration management service that enables content sellers to enable buyers to name their own price for content, based on what they saw as its value after using it. The logic for FairPay's services is based in Reisman's interpretation of the now-famous Long Tail diagram popularized by media executive and author Chris Anderson.

Reisman sees in his version of a long-tail diagram that the green area represents the zone in which sales typically occur for fixed-price content - that is, where perceived value is matched by the offered price. To the top end of the curve the red zone represents a significant group of buyers for whom an offered price is actually too low, based on them seeing it being more valuable or affordable. The orange zone to the right on the "long tail" represents the revenue not captured from the large number of potential, but unrealized, buyers who are unwilling to pay for content at that fixed price, based on them having a lower perceived value for the content that doesn’t match their desire or ability to pay.

FairPay's concept is fairly simple, but intriguingly powerful. The idea is to enable some people to obtain premium content without paying ahead of time, then to offer them the ability to pay at a specific price of their choosing if they provide a reasonable explanation of why that price would be fair for them. The system offers both pre-defined reasons to simplify response processing as well as free-form explanation opportunities. Based on the seller's perception of the fairness of the reasons given for a specific buyer. the buyer may be able to receive future access to premium content at similar prices. As Reisman notes, "The trick to making FairPay work is that it gives the seller a new ability to selectively manage the offer process by framing the offer and using feedback effectively to incentivize most buyers to pay at a reasonable level, and to screen out those who do not. To the extent that is done, the new revenue from their previously non-addressable market can become a very large total."

Of course, this model has to be able to match the right buyers with the right users, which is also part of the FairPay process. By collecting data on the buyer that helps the seller to understand their user role, it's possible that they can develop pricing models that can anticipate likely perceived value based on a much more detailed understanding of very specific market segments. So, for example, perhaps a scientific researcher focused on laser physics may have to access "must have" journals in his or her main line of work, and be willing to pay a high price for them, but then be less willing to pay for larger quantities of research from fields that are beyond their usual area of expertise but required to research a broad arena of hit-or-miss new market opportunities. On the consumer side, it could be that a streaming movie that someone thought was a dog was just right for them - and wind up enabling the provider to define matching content that they and people like them may be more willing to pay for more handsomely.

In other words, "pay as you exit" could lead to a profile-defined offer model for content already consumed of a particular genre, in essence a controlled free trial in which the seller is ensured revenues on an ongoing basis from a client with a much higher likelihood of successes in matching prices without costly negotiations or even more costly lost sales opportunities when people don't get a chance to experience the value of the product. The key to all of this is the profile data, of course, which is where Reisman may have his finger on a very valuable idea. FairPay is in essence real-time market research tool, enabling media providers to get more sophisticated insights into real willingness to pay for specific content under specific circumstances. This is one of the hardest accurate indicators to get out of market research on a questionnaire basis; only the real selling environment is able to tell us what people are really willing to pay for.

While Anderson's "Long Tail" model got publishers excited and began to get them realizing how much online bookselling could help to expose their broad catalog of offerings via search engines and other facilities, little has changed in the arena of pricing long tail or "hits" content as the result of that model; for most producers, the price is the price, and the market is the market. There's little curiosity in most media circles and even in many enterprise content markets about having highly flexible pricing regimes that could get more people excited about more content at the price that hits their right value points. In an era in which media was defined by homogeneous mass markets served by mass produced content, that was an understandable stance.

But the Web has enabled people to zero in on specific content sets rapidly with amazing accuracy, making "one size fits all" pricing less reflective of what content products represent to highly contextual markets. It's not just a matter of knowing when to knock down prices for whom; it's also a matter of knowing when to mark them up, because one person's trash may have become another person's treasure. In such highly contextual markets, supply is perfectly matched with demand when the right content is available instantly at the right time. While it's very early days for the FairPay model, it could turn out to be a tool that content producers could use to experiment with pricing in new and exciting ways that could lead to higher margins and deeper market penetration for their content - two concepts that could lead to more happy endings on their bottom lines.

Hey, if it worked for Darla, Spanky and Stymie, it could work for you.
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