Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content. Such an analysis may be performed using a graph or tabular data.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content. Such an analysis may be performed using a graph or tabular data.

Abstract: A method can be used to analyze the “real-estate” performance of content items within a network site. The method can comprise determining the click distance to reach each content item and determining the performance of each content item. The method can also comprise calculating a predicted value for performance based on statistical relationship between location and performance observed in a population of content items. The method can comprise comparing the predicted and actual performance. If a content item has an actual performance greater than its predicted performance, then it may be promoted to a better location in the site and the converse for poorer performing content. Such an analysis may be performed using a graph or tabular data.

Abstract: Methods and systems for identifying automated click fraud programs are provided. Upon receiving a request for presentation of a web page, the probability that the user is robotic is determined. The determined probability, along with historic behavior, if available, related to the requesting user, is used to determine a score that may be utilized to select advertisements for presentation to the user. If the score indicates a high likelihood that the user is robotic, an advertisement designed to solicit user behavior known to be associated with robots may be selected to confirm the suspicion. Alternatively, if the likelihood that the user is robotic is high enough, advertisement presentation may be largely suppressed. If, on the other hand, the score indicates a high likelihood that the user is human, a standard advertisement and/or an advertisement designed to solicit user feedback related to advertisements and/or publishers may be selected.

Abstract: Methods and data processing system readable media have been created to graph user clickstream data over a network or at a network site to yield meaningful and visually esthetic information. In one set of embodiments, the method can comprise (i) performing a significance test on data from a network log and generating significance results. The method can also comprise (ii) determining which of network addresses and clicktrails between network addresses meet a traffic flow criterion. The data that meet a significance criterion, traffic criterion, or both can form (iii) graphable addresses and relationships. The method can further comprise (iv) generating statistics about the graphable addresses and relationships. The method can still further comprise (v) generating a graph based on the statistics about the graphable addresses and relationships, and (vi) changing any or all of the traffic flow, significance criterion, and statistics being computed, and regenerating the graph.

Abstract: A data processing system-implemented method or data processing system readable medium can be used to model operating parameter(s) for a vendor. Detailed analysis of the impact of prices or other variables, on the demand of each item, is performed. These allow us to prune large numbers of prices or other variables which have little or no impact on a given item. After determining which prices and other variables are significantly related to an item, a more in-depth examination of that small list of variables may be performed. This in-depth examination will result in a set of final weighing factors, quantifying the effect of each on the item. The weighing factors for all other variables can be assigned a value of zero. By limiting the number of non-zero weighing factors, the time needed to generate all the weighing factors for a matrix (or matrices) is reduced.

Abstract: A method can be used to predict the purchasing potential of customers. In one embodiment, the prediction can be based in part on transactional data that is routinely collected by many businesses. An item preference model, a maximum spending model, a geographic model, and any combination of them can be used to make the prediction. The item preference model can be based on which items the customer prefers based on transactional data. The maximum spending model can use the daily maximum spending amount for a customer to determine potential. The geographic model may be based on distance or geographic indicate. Using any or all of the models, if the customer is spending below his or her predicted potential, he or she may be targeted for offers or other promotions.

Abstract: A computer-implemented method can be used to predict the behavior of a customer of a vendor at a future date. The method can comprise accessing data regarding the vendor's customers and generating timeseries information for at least one of the vendor's customers. The method can also comprise training a model to obtain weights. The training can be performed using at least some of timeseries information. The method can further comprise predicting the behavior of the first customer at the future date. The prediction can be performed using the weights in the model and at a frequency greater than monthly.