A system and method for generating advertisement automatically are provided. The system may comprise at least one computer-readable storage medium including a set of instructions; at least one processor in communication with the at least one computer-readable storage medium, wherein when executing the set of instructions, the at least one processor is configured to cause the system to generate a first plurality of ads, the first plurality of ads including a first plurality of advertisement elements and a first plurality of information components: transmit, via a network, the first plurality of ads to a first group of user terminals; determine at least one of a click-through rate, a number of impressions, or a conversion rate for the first plurality of ads; and analyze the at least one of the click-through rate, the number of impressions, or the conversion rate of the first plurality of ads.

The present teaching relates to method, system, and medium for profiling accounts. A plurality of identifiers are obtained, wherein each of the plurality of identifiers is associated with one of a device operated by a user and a platform on which the user consumes content in one or more media types. An account corresponding to the user is created, wherein the account represents the user across devices, platforms, and/or media types. A persistent identifier is generated for the account, wherein the persistent identifier links the plurality of identifiers. Consumption data related to the account with the persistent identifier is obtained. The consumption data is gathered from sources associated with the plurality of identifiers across different devices, platforms, or media types, and the account is profiled based on the obtained consumption data.

A method, product and system are provided for determining estimated quality scores in digital content distribution systems. The method comprises, based on monitoring of engagements and conversions, determining a segment-specific estimated quality score of a given traffic; determining an observation-based pair-specific quality score for the given traffic and for a specific campaign; and determining, based on the segment-specific estimated quality score and based on the observation-based pair-specific quality score, a pair-specific estimated quality score of the specific campaign for the given traffic segment indicating an estimated quality score of the specific campaign when presented in the given traffic segment.

In general, embodiments of the present invention provide systems, methods and computer readable media for ranking promotions selected for recommendation to consumers based on predictions of promotion performance and consumer behavior. In embodiments, a set of promotions to be recommended to a consumer can be sorted and/or ranked according to respective relevance scores representing a probability that the consumer's behavior in response to the promotion will match a ranking target. In embodiments, calculating scores is based on a relevance model (a predictive function) derived from one or more contextual data sources representing attributes of promotions and consumer behavior. In embodiments, an absolute relevance score represents an absolute prediction of a ranking target variable. In embodiments, absolute relevance may be used to determine personalized local merchant discovery frontiers; featured result set thresholding for impressions; and/or promotion notification triggers. In embodiments, predictive models based on gross revenue may be optimized using promotion category-dependent price boosting.

A method for real-time measurement of campaign effectiveness includes: storing device profiles, each including a hashed device identifier, geographic location, and transaction entries including a transaction date and transaction data; receiving a data request including a start date, end date, merchant identifier, a plurality of device identifiers, and, for each device identifier, a category identifier of a set of category identifiers; generating a hashed identifier for each of the device identifiers; identifying, a device profile for each hashed device identifier; calculating purchase behaviors for each category based on transaction data in transaction data entries that include a transaction date between the start date and end date that are included in each identified device profile that is associated with the respective category and includes a geographic location corresponding to the merchant identifier; and transmitting at least one of: the calculated purchase behaviors and one or more metrics based thereon.

Embodiments are disclosed for a method that may include accessing events in a field-searchable data store. The events may include raw machine data associated with a timestamp. The raw machine data may represent interactions between a mobile device and one or more network devices at a locale. The method may further include determining, based on the interactions, one or more geographic positions of the mobile device, and calculating a metric for the locale using the geographic positions.

Methods and devices for determining third party content to display on one or more webpages may include receiving an identification of at least one third party content for analysis. The methods and devices may include receiving third party content performance metric information for the at least one third party content based on an analysis of the at least one third party content. The methods and devices may include determining whether the third party content performance metric information is within a performance threshold level and sending a third party content control message that prevents the at least one third party content from presentation on the webpage based at least on the third party content performance metric information exceeding the performance threshold level.

In one embodiment, a server computing machine provides an interface operable to permit a first user to broadcast a live video to one or more second users. The server computing machine provides sponsored content items to the second users while also providing the live video. In response to receiving an indication that the first user selected an option to display a sponsored content item while broadcasting the live video, the server computing machine reduces the image size of the live video, superimposes the live video of reduced size on the sponsored content item to define a combined image having a sponsored portion on which the sponsored content item is displayed and a live portion on which the live video is displayed. The combined image is then sent to the one or more second users.

Method for monitoring the presence of a point-of-sale display in a shop, at the sight of consumers, the method comprising: acquiring signals from mobile devices by at least one signal sensor, sending one output from the signal sensor to an analyzing device, wherein the analyzing device: calculates an audience rate of the point-of-sale display on the basis of the output, and determines, on the basis of the audience rate, if the point-of-sale display is in the shop at the sight of consumers or not.

Approaches provide for or automatically optimizing sponsored content campaigns for a sponsored content provider for a particular consumption category across different content publisher networks. For example, performance data for sponsored content campaigns can be used to train a model for a consumption category to determine allocation scores that quantify a relationship between target audiences and the consumption category. In response to a content placement request to initiate a sponsored content campaign associated with the consumption category, allocation scores can be determined and used to dynamically determine an allocation of resources to appropriate audiences or segments based at least in part upon the trained optimization model. A campaign template can be generated that includes the target audience(s), sponsored content, and respective budget allocation, and any other information for the campaign. The campaign can proceed based at least in part on the campaign template, and performance of the campaign can be monitored and analyzed during the campaign to optimize the campaign dynamically.