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Location Data and Machine Learning Form the Hilt of New-Age Cross-Screen Advertising Attribution

Location Data and Machine Learning Form the Hilt of New-Age Cross-Screen Advertising Attribution John: TVadSync’s Multi-Touch Attribution model uses machine learning to correctly attribute performance value to cross-channel media. The attribution model is not trained with any historical or market data. It is totally blind as to whether it’s viewing a digital ad or a TV ad, and so no bias exists. It learns from mapping millions of touch-points across thousands of user journeys across TV and Digital media and calculating using a probabilistic method, which channel/placement/creative, or TV spot were most influential at driving conversions. Traditional attribution models were based on pre-determined, often position based biases which are prone to human error, and positively skew lower funnel advertising such as digital media, leading to inaccurate attribution and huge optimization inefficiencies. By taking the human element out of the attribution model, the machine learning-based approach is the only way to measure cross-channel media and is providing huge gains to TVadSync’s clients. What are the opportunities and risks you foresee in the way location data is shaping for 2020-2025? How do you prepare for these disruptions? Thomas: The focus of location data is moving away from individuals and more toward trends-based data.

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