Yuzi-Liu / CTR-Analysis

This project broadly deals with location-based mobile marketing, using data from a location-based marketing agency which handles geo-fencing campaigns on behalf of advertisers. I choose to use a random sample for two campaigns of a single advertiser – AMC Theaters. The advertising impressions are inserted into the mobile app being used on the device. The data include the following elements: impression size (e.g., 320x50 pixels), app category (e.g., IAB1), app review volume and valence, device OS (e.g., iOS), geo-fence lat/long coordinates, mobile device lat/long coordinates, and click outcome (0 or 1).

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CTR-Analysis

This project broadly deals with location-based mobile marketing, using data from a location-based marketing agency which handles geo-fencing campaigns on behalf of advertisers. I choose to use a random sample for two campaigns of a single advertiser – AMC Theaters. The advertising impressions are inserted into the mobile app being used on the device. The data include the following elements: impression size (e.g., 320x50 pixels), app category (e.g., IAB1), app review volume and valence, device OS (e.g., iOS), geo-fence lat/long coordinates, mobile device lat/long coordinates, and click outcome (0 or 1).

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This project broadly deals with location-based mobile marketing, using data from a location-based marketing agency which handles geo-fencing campaigns on behalf of advertisers. I choose to use a random sample for two campaigns of a single advertiser – AMC Theaters. The advertising impressions are inserted into the mobile app being used on the device. The data include the following elements: impression size (e.g., 320x50 pixels), app category (e.g., IAB1), app review volume and valence, device OS (e.g., iOS), geo-fence lat/long coordinates, mobile device lat/long coordinates, and click outcome (0 or 1).


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