Abstract: An advertisement request allocation platform includes: a monitoring module comprising an offer rate builder, the offer rate builder configured to periodically determine a utilization rate using a plurality of candidate time windows, wherein the utilization rate for a combination of a group of advertisement requests and a demand-side platform (DSP) comprises a rate at which the DSP utilizes the group, the offer rate builder further configured to periodically determine for the combination a measure of variability of the utilization rate; and an offer module operably connected to the DSP; and an offer module configured to offer an advertisement request to the DSP, wherein the offer module is further configured to adapt a rate at which advertisement request is offered to the DSP based on the utilization rate for the DSP.

Abstract: A hybrid, optimized exchange is provided, the hybrid, optimized exchange operably connected to a demand-side platform (DSP), the hybrid, optimized exchange further operably connected to a supply-side platform (SSP), the hybrid, optimized exchange configured to receive an advertising request, the hybrid, optimized exchange further configured to conduct, using a bid floor, a hybrid, optimized DSP auction of the advertising request among a plurality of DSPs, thereby generating a winning DSP that makes a winning DSP bid in an automated advertising auction system after the SSP initiates an SSP auction of the advertising request, the hybrid, optimized exchange optimizing the bid floor provided to the plurality of DSPs in the hybrid, optimized DSP auction and simultaneously optimizing a shading factor used by the hybrid, optimized exchange to place a hybrid, optimized bid on behalf of the winning DSP in the SSP auction.

Abstract: A measurement platform configured to measure performance of an advertisement, the measurement platform comprising a demand-side platform (DSP), the measurement platform configured to send to an end user a survey configured to measure the advertisement's performance on behalf of a client DSP, wherein the measurement platform is further configured to update an exposed group of exposed users, wherein the measurement platform is further configured to update a control group of end users with similar statistical properties to the exposed group except that the control users have not seen the winning advertisement, wherein the measurement platform is further configured to send the survey to the end user, wherein the measurement platform is further configured to receive a survey response from the end user, wherein the measurement platform determines a metric measuring performance of the winning advertisement, wherein the measurement platform is further configured to send the metric to the client DSP.

Abstract: The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Abstract: The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Abstract: The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Abstract: The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Abstract: The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser's campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in the case of winning the auction. For losing bids, the win price remains censored. In this invention, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. This yields a boost in predictive performance in comparison to classic linear censored regression.

Abstract: A system for collecting brand awareness and advertising campaign performance results in real-time. Embodiments allow the system to adapt (e.g., machine learning) to target advertisements to users that are most likely to be influenced by exposure to a brand awareness advertising campaign, and present results, in real-time, via a data exchange for an advertiser to monitor performance and benchmark performance against similar campaigns across the industry.