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Each network specific passive or active engagement is compared within our trained model, which bases its learnings on historical Organic and Paid Data to generate Beta weights (constantly updating value weights) for each data point independently on a per post basis.
Reach and impressions for non-authenticated accounts are calculated through machine learning.
Impressions and brand awareness are the most common metric used
to anchor advertising and marketing objectives. Our model backs into impressions to deliver valuation that aligns with an advertising equivalency for CPA/CPE.
Brand equities (i.e. post content data, or specific brand value factors, that appear within the body of a post that align with brand name, brand mention, tagline, keyword, or hashtag) are weighted and adjusted based on post performance, and provide an input value for the raw brand value score. They do not hold an independent numerical value.
Based on channel, placement and format, visual and audio metrics are derived via an image or video scanning process. Detections of all visually branded elements within a post are valued based on calculations of size, duration (not valid for image), location/position, and clarity (visibility and clutter).
Broadcast valuation uses the CPM value of the broadcast and the viewership to determine the overall value of the entire broadcast. The CPM rates are based on the type of broadcast (local, regional, national etc.). The viewership is recorded from MVP’s nationwide database encompassing more than 3 million households. Streaming viewership data is taken in one to five minute intervals and valuation is determined accordingly.
Brand value begins with the overall video value and portions value to the visible brands within the broadcast or stream. The portions are based on the size of the logo (larger is more valuable), the clarity of the logo (brands have to be visually recognizable), the clutter of the space (more brands in a single shot lead to less perception), and the location of the logo. Optimal logo locations include the top left and center of the viewing area. During a stream, the viewership at the moment the logo is visible is used to determine the value for the brand.
Within the underdog’s surrounding DMAs, viewership stays relatively stable, as evidenced by Oral Roberts' fans; as a whole, the region’s audience size increased by just 2% on average. This is likely due to most viewers in the area already being tuned in to see how their local team performs.
Historically successful basketball programs such as UNC and Wisconsin draw in significantly higher viewership on a nationwide scale than small schools; however, schools within highly dedicated regions such as Ohio State and Texas Tech also yield above-average viewership within their respective markets.
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Audiences who might not otherwise have tuned in become intrigued by the prospect of an upset, prompting them to either turn their TVs on, or transfer over from a concurrent matchup. From the start of the 2nd half through overtime, total viewership tripled during North Texas’ unexpected defeat of Purdue, as viewers increasingly flipped over to witness the legitimacy of the Mean Green’s upset attempt.
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Viewers in the top-seeded team’s region are more likely to anticipate an easy victory, and therefore not feel the need to tune in initially. Within the underdog’s surrounding DMAs, viewership stays relatively stable, possibly due to most viewers in the area already watching to see how their local team performs.
Despite being overshadowed by numerous colleges in the greater Dallas area, by the end of North Texas’ upset, viewership in the region had risen by 140%. In doing so, the team managed to draw in above-average viewership in Dallas, the 5th-largest market in the country, where they otherwise likely would have gone unnoticed.
