A method of an attribution server. The method determines publishing channels for advertisements in a marketing campaign to analyze their marketing effectiveness for purchasable items using a processor and a memory of the attribution server. Data points are associated with users. A K-th order attribution model is constructed. Independent and dependent variables of the attribution model are associated with various types of marketing data. An observation matrix and a conversion vector are determined. A regression analysis is performed with refining steps. Insignificant second order cross terms of the attribution model are identified and removed. A modified K-th order attribution model is constructed. Another regression analysis is performed to find optimal model parameters. The attribution server computes attribution scores associated with the publishing channels based on the attribution models and the optimal model parameters from regressions, and communicates the attribution scores to a marketer client through a network upon a request.

A telecommunication system implements a method for providing a targeted on-line advertisement to a user accessing a content provider node of the system. An ad is requested from a user node. A content provider is identified at a right of first refusal ad service based on the ad request. At least one demographic corresponding to the user node is determined. Whether an ad corresponds to the determined demographic is determined. A default ad service is determined based on the content provider. The ad request is passed to a default ad service. Retrieved content is processed.

A process is disclosed for obtaining selected item data from a media processor, wherein the selected item data indicates an item used by an actor in a video data stream. The video data stream includes data representing the item used by the actor and icon data including multiple icons associated with a number of mobile devices. Selected icon data is obtained from the media processor, indicating a selected one of the number of mobile devices. Advertising data for the selected item is provided to the selected one of the number of mobile devices via a communication network. The advertising data is sent when the selected one of the number of mobile devices comes in proximity with a merchant having the selected item in stock. A system is disclosed for performing the process. A machine-readable device is also disclosed for use by the system and process.

A method includes determining that an error has occurred. The error includes a hardware error in a computer, a network error in a network connected to the computer and/or a software error for software executing on the computer. The computer determines that the error has occurred. The method includes identifying a type of computer system in which the error has occurred. The computer system type includes a manufacturer and/or a brand for the computer. The method includes identifying, in response to determining that the error has occurred, an advertisement for a product or service relating to resolution of the error and to the type of computer system. The computer identifies the advertisement and generates an error message. The error message includes the advertisement and information regarding the error. The computer displays the error message with the advertisement on an electronic display connected to the computer.

A clustering and recommendation machine determines that an item is included in a cluster of items. The machine accesses item data descriptive of the item. The machine accesses a vector that represents the cluster and calculates the likelihood that the item is included in the cluster, based on the item variable and the probability parameter. The machine determines that the item is included in the cluster, based on the likelihood. The machine also recommends an item to a potential buyer. The machine accesses behavior data that represents a first event type pertinent to a first cluster of items. The machine calculates a probability that a second event type pertaining to a second cluster of items will co-occur with the first event type. The machine identifies an item from the second cluster to be recommended and presents a recommendation of the item to the potential buyer.

A sales promotion apparatus includes a first visiting customer information acquisition unit, a different store information presentation unit, a second visiting customer information acquisition unit, and a visiting customer match determination unit. The first visiting customer information acquisition unit acquires, from a first visiting customer information acquisition apparatus provided in the one store, biological information of a customer visiting the one store. The different store information presentation unit outputs, to an output apparatus provided in the one store, information regarding a different store. The second visiting customer information acquisition unit acquires, from a second visiting customer information acquisition apparatus provided in the different store, biological information of a customer visiting the different store. The visiting customer match determination unit determines whether there is a match between the biological information acquired by the first visiting customer information acquisition unit and the information acquired by the second visiting customer information acquisition unit.

The present disclosure describes various embodiments of methods, systems, and machine-readable mediums which may be used in conjunction with a campaign for distributing content to users of the social network. Among other things, embodiments of the present disclosure provide a number of advantages over conventional systems for content distribution, including a simplified targeting process and increased reach (i.e. distribution) for content providers among users of a social network, as well as improving the utilization of an inventory of content and higher and more efficient engagement with such content by users of the social network.

A system and method is provided for adding at least one advertisement to a personal communication and providing additional communication data to a recipient that interacts with the advertisement. A sender network device communicates with an advertising application operating on a Web site to generate a personal communication containing at least one advertisement, where the at least one advertisement is selected from a palette of advertisements. In one embodiment of the present invention, the sender has control over advertisements that are displayed together with the personal communication by allowing the sender to delete (or remove) an advertisement from either the at least one advertisement or the palette of advertisements. If a displayed advertisement is interactive, and the advertisement is interacted with, the advertising application will provide the recipient with additional communication data in a format that can be understood by the recipient network device.

A system and method for identifying whether certain web-based media or web-based videos are likely to become popular is provided. Videos that are identified as having a strong likelihood of becoming popular with a particular demographic group are paired to advertisements or other media appropriate for the particular demographic group. Videos that are likely to be popular are identified by measuring early input rates such as request rates, replay rates, comment rates, forwarding rates and reply rates. Input rate patterns including pattern segments correlated to inputs from a particular demographic group are identified.

A real-time messaging platform allows advertiser accounts to pay to insert candidate messages into the message streams requested by account holders. To accommodate multiple advertisers, the messaging platform controls an auction process that determines which candidate messages are selected for inclusion in a requested account holder's message stream. Selection is based on a bid for the candidate message, the message stream that is requested, and a variety of other factors that vary depending upon the implementation. The process for selection of candidate messages generally includes the following steps, though any given step may be omitted or combined into another step in a different implementation: targeting, filtering, prediction, ranking, and selection.