Abstract: Prioritization techniques and systems are described that utilize a historical purchase sequence and customer features to prioritize products and services to generate product and service recommendations. In an example, feature data describing a customer and historical purchase data for the customer is received that indicates products or services purchased by the customer. The historical purchase data further includes indicators of when the products or services were purchased by the customer. Then, probabilities of future purchases by the customer of additional products are determined by classifying the additional products using a multiclass classification. The multiclass classification is based on the historical purchase data and the feature data describing the customer. Next, a ranking of the additional products is generated based on the determined probabilities of future purchases. The ranking of the additional products is output in a user interface based on the determined probabilities.

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

Abstract: Dynamic Hierarchical Empirical Bayes techniques and systems are described that are implemented to control output of digital content. In one example, a system identifies splitting variables included in data. An amount of loss is then determined for each of the identified splitting variables by the system using a loss function. Based on the determined amounts of loss, the system selects at least one splitting variable from the plurality of splitting variables that are to be used to partition data in a respective node, e.g., a parent node to form a plurality of child nodes. The system, for instance, may select the splitting variable that minimizes the cost, i.e., has the lowest amount of cost. The selected splitting variable is then employed by the system to generate at least one hierarchical level of the hierarchical structure of the statistical model by partitioning data from the parent node into respective child nodes.

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

Abstract: Certain embodiments involve a model for predicting user behavior. For example, a system accesses user behavior data indicating various users' behaviors during intervals over various periods of time and target behavior data indicating a particular user behavior. The system associates each user with a label that indicates whether a user performed a particular action during or after a time period based on the target behavior data. The system uses the user behavior data to train various deep Restricted Boltzmann Machines (“RBM”) to generate representations of each user over each period of time that indicate the user behavior over the time period. The system generates a predictive model by connecting the RBMs into a deep recurrent neural network and uses the target behavior data associated with each user, along with the representations of each user, as input data to train the deep recurrent neural network to predict user behavior.

Abstract: Methods, systems, and computer readable storage media are disclosed for generating joint-probabilistic ensemble forecasts for future events based on a plurality of different prediction models for the future events. For example, in one or more embodiments the disclosed system determines error values for various predictions from a plurality of different prediction models (i.e., “forecasters”) for previous events. Moreover, in one or more embodiments the system generates an error probability density function by mapping the error values to an error space and applying a kernel density estimation. Furthermore, the system can apply the error probability density function(s) to a plurality of predictions from the forecasters for a future event to generate a likelihood function and a new prediction for the future event.

Abstract: Systems and methods disclosed herein use one or more auxiliary time series to more accurately identify change points in a target time series. This involves receiving data for the target time series and one or more auxiliary time series, where the one or more auxiliary time series have a relationship with the target time series. A combined auxiliary time series is generated based on the relationship between the target time series and the one or more auxiliary time series and the change point is detected for the target time series based on the target time series and the combined auxiliary time series. In one embodiment, time series data is received on an on-going basis. Recent time series data for the target time series and the one or more auxiliary time series is identified and used to detect the change point. The change point can be detected without using time series data older than the recent time series data.

Abstract: A bid management system generates estimated performance metrics at the bid unit level to facilitate bid optimization. The bid management system includes a hierarchical feature selection and prediction approach. Feature selection is performed by aggregating historical performance metrics to a higher hierarchical level and testing features for statistical significance. Features for which a significance level satisfies a significance threshold are selected for prediction analysis. The prediction analysis uses a statistical model based on selected features to generate estimated performance metrics at the bid unit level.

Abstract: The present disclosure relates to a media attribution system that improves multi-channel media attribution by employing discrete-time survival modeling. In particular, the media attribution system uses event data (e.g., interactions and conversions) to generate positive and negative conversion paths, which the media attribution system uses to train an algorithmic attribution model. The media attribution system also uses the trained algorithmic attribution model to determine attribution scores for each interaction used in the conversion paths. Generally, the attribution score for an interaction indicates the effect the interaction has in influencing a user toward conversion.

Abstract: Profiles characterizing each of a plurality of consumers are received. Thereafter, each profile is associated with one of a plurality of customer segments (e.g., matched pairs, etc.). Thereafter, a coupon effectiveness index is determined for each of the plurality of consumers for an offering based on the associated customer segment. The coupon effectiveness indices model characterizes causal effects estimates determined using historical data of purchases of individuals having varying coupon treatments for the offering. Subsequently, provision of at least a portion of the determined coupon effectiveness indices is initiated. Related apparatus, systems, techniques and articles are also described.

Abstract: In various implementations, analytics data is received that indicates performance of bid targets for historical bids made in one or more content delivery auctions. Baseline prediction models are maintained for the bid targets. The baseline prediction models use the analytics data to predict performance of the bid targets in one or more future instances of at least one content delivery auction. A presentation context factor model is maintained that provides an adjustment factor that quantifies a contribution of a subset of a plurality of presentation context factors associated with the bid targets to performance of the bid targets based on predicted values from the baseline prediction models. A contextual predicted value is computed using the adjustment factor for the subset of the plurality of presentation context factors. A performance prediction is transmitted to a user device and is based on at least the contextual predicted value.

Abstract: Systems and methods disclosed herein use one or more auxiliary time series to more accurately identify change points in a target time series. This involves receiving data for the target time series and one or more auxiliary time series, where the one or more auxiliary time series have a relationship with the target time series. A combined auxiliary time series is generated based on the relationship between the target time series and the one or more auxiliary time series and the change point is detected for the target time series based on the target time series and the combined auxiliary time series. In one embodiment, time series data is received on an on-going basis. Recent time series data for the target time series and the one or more auxiliary time series is identified and used to detect the change point. The change point can be detected without using time series data older than the recent time series data.

Abstract: Embodiments of the present invention relate to allocating online advertising budget based on return on investment (ROI). Allocating an advertising budget based on ROI can facilitate advertisement bidding such that ROI can be optimized for the advertiser. In implementation, an online advertising budget is generally provided in connection with a budget time duration during which the online advertising budget is to be used. In determining a budget allocation in association with a particular feature, an indication of a ROI for each set of feature values is determined. The indication of the ROIs associated with each set of feature values can be used along with the advertising budget to identify an optimal allocation of the online advertising budget for the budget time duration. In some cases, constraints may also be applied to optimize budget allocation among the feature values.

Abstract: An ensemble model is described that is usable to predict revenue metrics for one or more keywords. The ensemble model may be formed using both a historical model and a user behavior model. In one or more implementations, weights are assigned to the historical model and/or the user behavior model based on one or more criteria. Various processing techniques of the ensemble model may utilize the historical model and the user behavior model to predict revenue metrics for one or more keywords.

Abstract: Performance data for online advertisement creatives may be received. A hierarchical model of the online advertisement creatives may be generated based on correlations among the online advertisement creatives. The hierarchical model may be used to estimate a respective performance value for each of at least some of the plurality of online advertisement creatives based on the received performance data. A creative quality score may be determined, for those online advertising creatives whose performance values were estimated, based on the estimated performance values.

Abstract: A censored observation for an online advertising campaign may be received for a first given time period. It may be determined that an amount spent on the online advertising campaign met a budget constraint such that the online advertising campaign was interrupted during the first given time period. Based on the received censored observation and one or more campaign parameters for the first given time period, a predictive model for predicting the result of a new online advertising campaign may be generated.

Abstract: Methods and systems for testing, comparing, and optimizing creatives with multiple factors in digital advertising is presented. Experiments are designed for testing a plurality of factors combined to form a creative. Ad campaigns are launched or continue according to the design and the creatives' campaign performance data is collected. Statistical modeling and hypothesis testing are used to predict the performance of the creatives based on the performance data. The creatives are compared based on the predictions and either activated or deactivated based upon their relationship to statistical confidence levels. All the stages are executed automatically and iteratively.

Abstract: Methods and related system are described for making decisions. A described method includes selecting a choice from the available choices, receiving an outcome relating to the selected choice, and automatically learning from the received outcome by incorporating the received outcome into subsequent steps of selecting a choice. The method may also include calculating estimated probabilities associated with the each choice using Bayesian networks. The automated learning can be based on a learning rate which is variable with time, and influences the degree on which prior outcomes are relied upon when calculating an estimated probability associated with a choice. The learning rate can be a function of time and an estimate of drift of the probability associated with the selected choice.

Abstract: A brazing system for brazing component members of a workpiece has a brazing chamber an inside of which is made a heating space of a volume corresponding to the workpiece, a radiant heating means provided with a plurality of heating sources which are positioned so as to correspond to a plurality of regions into which two facing surfaces of the workpiece are respectively divided, a convection heating means for circulating a heated inert gas to the heating space so as to heat the workpiece, and a control means for controlling the operation of the heating sources and the circulation of the inert gas. Each heating source is independently controlled by the control means, and the convection heating means circulates the inert gas so as to reduce a temperature difference of the workpiece caused by the heating sources.