Abstract: Technologies are provided for automated generation of directed content campaigns. The generated campaign can be optimized for performance. In some embodiments, a group of variations of attributes that define a directed content campaign can be generated and allocated traffic weights for respective impressions of the directed content campaign in a media outlet channel. The traffic weights can then be iteratively updated until a termination criterion is satisfied. At each iteration, the traffic weight can be updated by applying a machine-learning model to current performance metric values of respective impressions corresponding to the traffic weights. After termination of the updates to the traffic weights, a particular set of variations having traffic weights exceeding a threshold can be selected as directed content campaign having satisfactory performance. Those variations can be supplied to a requestor device for subsequent utilization.

Abstract: A method and system for prioritizing accounts is provided. The method includes receiving operational metrics and data values associated with the operational metrics. The data values are aggregated and noisy data is removed from the operational metrics. Statistical averages associated with a performance of accounts are calculated and each account is modeled as a collection of operational performance variables and key performance indicators A ranking list is generated and a minimum number of independent operational parameters necessary are calculated. Pairwise ranks {r} of the accounts are calculated and values associated with a tolerance limit epsilon and selected.

Abstract: A method and system for prioritizing accounts is provided. The method includes receiving operational metrics and data values associated with the operational metrics. The data values are aggregated and noisy data is removed from the operational metrics. Statistical averages associated with a performance of accounts are calculated and each account is modeled as a collection of operational performance variables and key performance indicators A ranking list is generated and a minimum number of independent operational parameters necessary are calculated. Pairwise ranks {r} of the accounts are calculated and values associated with a tolerance limit epsilon and selected.

Abstract: A system for prioritizing accounts is provided. The method includes receiving operational metrics and data values associated with the operational metrics. The data values are aggregated and noisy data is removed from the operational metrics. Statistical averages associated with a performance of accounts are calculated and each account is modeled as a collection of operational performance variables and key performance indicators A ranking list is generated and a minimum number of independent operational parameters necessary are calculated. Pair wise ranks {r} of the accounts are calculated and values associated with a tolerance limit epsilon and selected.

Abstract: A method and system for prioritizing accounts is provided. The method includes receiving operational metrics and data values associated with the operational metrics. The data values are aggregated and noisy data is removed from the operational metrics. Statistical averages associated with a performance of accounts are calculated and each account is modeled as a collection of operational performance variables and key performance indicators A ranking list is generated and a minimum number of independent operational parameters necessary are calculated. Pairwise ranks {r} of the accounts are calculated and values associated with a tolerance limit epsilon and selected.