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The Power of Digital Revenue Allocation

by on December 14, 2010 in ARPU, Demand Map

Demand Map

Posted by: Matt Shanahan During the last five weeks, I have written extensively about the Demand Map™, a quantitative lens for digital revenue optimization.  The Demand Map™ posts describe how the unit cost of engagement can be calculated (i.e., engagement divided by revenue during the period) and used to identify which licenses or ad orders are incorrectly priced.  The chart to the right depicts a example of the Demand Map™.  In this chart according to my previous posts, each of the data points could be either a sold license or ad order and the associated engagement.  The reality is that the Demand Map™ has more power than just charting licenses and ad orders! Unlocking the true power of the Demand Map™ involves digital [...]

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Finding Disparities in Ad Rates Using the Unit Cost of Engagement

by on December 2, 2010 in Advertising, CPM, Demand Map

"Demand Map"

Posted by: Matt Shanahan This is the second in a series on the importance and use of unit cost of engagement in advertising. In ad-supported media, a pricing disparity is defined as an advertiser paying too much or too little for audience engagement compared to their peer advertisers. Pricing disparities are often hidden because ad rates are charged based on impression quantity (i.e., CPM pricing) which doesn’t account for actual engagement by users. Because brand recall and click-through rates are directly correlated with engagement, the unit cost of engagement (i.e., CPS pricing) can be used to uncover disparities and opportunities for digital revenue optimization. One of the easiest ways to visualize pricing disparities is to plot each ad order according to the order price and the engagement delivered for that order. The typical distribution of orders [...]

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Demand Map™: A Quantitative Lens for Revenue Optimization in Paid Content

by on November 2, 2010 in Demand Map, Pricing

Posted by: Matt Shanahan In the book Super Crunchers, Ian Ayres looked at hundreds of tests evaluating how data-based decision making (i.e., quantitative) fares in comparison with  experience- and intuition-based decisions (i.e., qualitative).  We were interested in doing the same thing regarding license revenue optimization in paid content. When a publisher designs a license for paid content (i.e., packaging and pricing of access to media and information), they do so with an expectation of how much media or information will be consumed (i.e., the level of engagement).  Typically, the license is defined in terms of sections, volume, numbers of users and usage rights.  When a new license is introduced, the initial price is usually based on previous experience and intuition of [...]

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