Real-time media valuation system and methods
Various exemplary embodiments include a valuation system to measure sponsorship exposure in social media, including the ability to use multiple valuation methods, such as CPV, CPM, and CPE, integrated into the valuation system, in a real-time manner, while aggregating the most up-to-date data. It may categorize the social media by media type, image, video or text, and may run different valuation strategies based on the media type. It may adapt a valuation strategy based on the source platform of the publication, and detect the sponsorship exposures and inputs into the valuation method, as one or more factors to the valuation system, in the real-time manner. Additionally, granular valuation may be supported, given the output from the AI-driven system, on brand, asset, scene, and media exposure types. It also supports valuation on a real-time ad rate, a device factor, customization based on user configurations, e.g., discounted factors, and/or supports live stream.
1 . A method for valuing media content exposure, the method comprising:
detecting a media item on a media platform;
categorizing the media item into a media type;
selecting at least one valuation method from one or more recognized valuation methods;
sending the media item to an AI-driven brand-spotter system, the brand-spotter system configured to:
detect brands, assets, scenes, and active versus passive exposure types in the media item using computer vision coupled with machine learning; and
generate metrics of the detected brands, assets, scenes, and the active versus passive exposure types; and
valuing one or more of the media items, the detected brands, assets, and scenes, and the active versus passive exposure types present via one or more of customizable or preset cost per metric formulas, custom or preset valuations, and in relation to auction prices for advertising rates auction prices;
wherein the valuing the one or more of the media items, the detected brands, assets, and scenes, and the active versus passive exposure types present via the one or more of customizable or preset cost per metric formulas, custom or preset valuations, and in relation to the auction prices for the advertising rates auction prices is a granular valuation, the granular valuation including adapting to changes in performance signals and including a strength meter, the strength meter being an indicator of strength of the detected brands based on a visual appearance of the media item and visibility of the detected brands on different types of devices.
2 . The method of claim 1 , further comprising detecting a posted media item on an application.
3 . The method of claim 1 , the metrics including a visual appearance.
4 . The method of claim 1 , the metrics including occlusion.
5 . The method of claim 1 , the metrics including blurriness.
6 . The method of claim 1 , the metrics including contrast.
7 . The method of claim 1 , the metrics including size.
8 . The method of claim 1 , the metrics including appearance duration.
9 . The method of claim 1 , the metrics including viewers present at time of exposure.
10 . The method of claim 1 , the metrics including screen share of the brands.
11 . The method of claim 1 , the media type including text.
12 . The method of claim 1 , the media type including an image.
13 . The method of claim 1 , the media type including a video.
14 . The method of claim 1 , the media type including a three-dimensional media item.
15 . The method of claim 1 , the media type including an asset.
16 . The method of claim 1 , the media type including an asset set in augmented reality or in virtual reality.
17 . The method of claim 1 , wherein categorizing the media item into the media type further comprises identifying whether the media item is a text post, image post, video post, or a carousel post containing multiple media types.
18 . The method of claim 1 , wherein the AI-driven brand-spotter system further generates device visibility profiles for each detected brand, asset, or scene in the media item.
19 . The method of claim 1 , wherein the granular valuation includes determining a brand value in a video post by multiplying a video post value by a video brand factor, the video brand factor comprising a screenshare factor, a time on screen factor, and a quartiles factor.
20 . A non-transitory computer readable medium having instructions thereon for execution by a processor, the instructions comprising a method for generating a monetary valuation of sponsorship exposure in a media item, the method comprising:
detecting a media item on a media platform;
categorizing the media item into a media type;
selecting at least one valuation method from one or more recognized valuation methods;
sending the media item to an AI-driven brand-spotter system, the brand-spotter system configured to:
detect brands, assets, scenes, and active versus passive exposure types in the media item using computer vision coupled with machine learning; and
generate metrics of the detected brands, assets, scenes, and the active versus passive exposure types; and
valuing one or more of the media items, the detected brands, assets, and scenes, and the active versus passive exposure types present via one or more of customizable or preset cost per metric formulas, custom or preset valuations, and in relation to auction prices for advertising rates auction prices;
wherein the valuing the one or more of the media items, the detected brands, assets, and scenes, and the active versus passive exposure types present via the one or more of customizable or preset cost per metric formulas, custom or preset valuations, and in relation to the auction prices for the advertising rates auction prices is a granular valuation, the granular valuation including adapting to changes in performance signals and including a strength meter, the strength meter being an indicator of strength of the detected brands based on a visual appearance of the media item and visibility of the detected brands on different types of devices.