Leaf Group Ltd.

Jeremy Daw

Antonio Magnaghi

Jeong-Yoon Lee

Embodiments of the present disclosure help content providers maximize the profitability of the online content they produce by providing an accurate, inexpensive, and timely quantitative estimate of the revenue the content is likely to generate. Various embodiments can refine estimates based on real-time or near-real-time data in conjunction with historical pricing data, thereby further improving the accuracy of the revenue predictions. A computer-implemented method according to one embodiment of the present disclosure comprises receiving, by a computer system, information regarding a topic; identifying, by the computer system, a format for content associated with the topic; and determining a score indicative of predicted revenue generated from future content in the identified format associated with the topic, wherein determining the score is based on a revenue model under which revenue from the future content would be generated.

Systems and methods for predicting revenue for web-based content

Embodiments of the present disclosure help content providers identify popular topics based on Internet search data, as well as to quantify the popularity of content addressing those topics. A computer-implemented method according to one embodiment of the present disclosure comprises receiving search data by a computer system; identifying a category related to at least a portion of the search data; determining, by the computer system and based on the search data, a topic for content associated with the identified category; and determining, by the computer system, a score indicative of a level of popularity for the topic, wherein determining the score is based on the search data and on one or more formats for the content associated with the topic.

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Venice, CA, United States of America

Jeremy Daw