The service is being marketed it as SaaS (software as a service). Matt Shanahan, senior VP-marketing and strategy at Scout Analytics, described how the technology works and how business publishers can benefit from it.
“Demand Map is a predictive pricing solution for digital publishers,” he said. “Demand Map integrates data about how much a customer uses a publisher's site versus how much the customer paid for his subscription. So, if a customer is paying a subscription fee to the publisher, how do the payments [they] make match up to the actual usage for that subscription compared to other customers?”
Using this analysis, Shanahan said, Demand Map software determines a price appropriate to each customer and identifies whether or not the customer is currently overpaying or underpaying.
Demand Map is designed to calculate a unit price per user engagement with the content. From these data, the software determines a median unit price that customers should be charged based on their level of usage.
“Demand Map then figures out from the median what should be the discounting at the upper end,” Shanahan said. “It helps publishers figure out what should be the list price for of each level of engagement. For example, what is the value in downloading a white paper or reading an article? What should the unit price of one article be? Demand Map figures out what the average user looks like and what he is paying for that media usage by taking the number of downloads—which is the unit of value—and dividing it by the price paid to calculate the unit price. Then the publisher can start to establish pricing tiers based on unit price.”
Shanahan said that the Demand Map technology is easily integrated into publishers' systems. “All they need to do is include a Java script that we provide and put that into their website, and Scout Analytics starts generating the demand maps from that,” he said. “It's very similar to Google Analytics.”
Shanahan said Demand Map's software is designed to increase a publisher's yield per user. “The benefit is to increase average revenue per user 10% to 15%,” he said. “The problem that it overcomes right now is that publishers' revenue data, from their billing systems, and their usage data, from their websites, aren't integrated at all; so they can't do this analysis to figure out where the pricing optimizations are.”