Methods and systems of securely exchanging customer data are provided. The exchange may be done in privacy-preserving and/or privacy-protective fashion. Customer data from multiple merchants, including transaction data for a first product between a first customer and a first merchant, is maintained in a customer data pool by an e-commerce platform. When a second merchant associated with a second product requests new customer data, a product (i.e., the first product) relevant to the second product is identified. A collection of customers who indicated interest in the first product (including the first customer) are identified from the customer data pool, and a target audience is generated proportional to customer data exchanged by the second merchant with the customer data pool. Communications from the second merchant are then provided to the target audience without disclosing their customer data to the second merchant.

An online system optimizes for longer attribution window conversions with an additive decomposition model by predicting the probability that a predefined action happens given an impression/click. The online system receives a content item from a content provider for display to a target user, and predicts a probability that a target user will convert given an interaction with the content item by the target user. The online system computes, by a first trained model, a short-term conversion probability of a conversion event happening within a first conversion window after the interaction. The online system computes, by a second trained model, a long-term conversion probability of the a conversion event happening within a second conversion window after the interaction, the second conversion window being longer than the first conversion window. The online system computes the conversion probability given the interaction based on the short-term conversion probability and the long-term conversion probability.

Systems and methods related to resource distribution for a fleet of machines are disclosed. A system may include a fleet of machines each having an associated resource capacity and a resource requirement to perform a task. The system may further include a controller having a resource requirement circuit to determine an aggregated amount of the resource requirement and an aggregated amount of the resource capacity. A resource distribution circuit may adaptively improve, in response to an aggregated amount of the resource capacity, an aggregated resource delivery of the resource.

Systems and methods described herein can automatically route an inbound call from an identified customer to one of a plurality of agents, the agent being selected on the basis of likelihood of a favorable outcome. The method determines a predictive model appropriate for the identified customer, with model variables including call center data, and targeted marketing data based upon risk data for the customer. An analytical engine calculates outcome predictions by applying the predictive model to values of model variables over a recent time interval. In a time-series analysis, this calculation is repeated while dynamically adjusting the recent time interval, until identifying a call routing option that satisfies a favorable outcome criterion. This method may be used to select the agent to handle the incoming call, and optionally to select a product for that agent to discuss with the identified customer.

An example computing device includes: a processor; and a system memory, the system memory including instructions which, when executed by the processor, cause the computing device to: identify a financial account associated with debit card use; parse financial transactions associated with the financial account to identify debit card transactions; categorize the debit card transactions into categories based upon types of merchants; map the categories to incentives associated with a credit card; and present potential savings associated with the incentives.

This paper presents a practical method for measuring the impact of multiple marketing events on sales, including marketing events that are not traditionally trackable. The technique infers which of several competing media events are likely to have caused a given conversion. The method is tested using hold-out sets, and also a live media experiment for determining whether the method can accurately predict television-generated web conversions.

Systems and methods of dynamically varying the intensity of providing content items in a remarketing campaign based on tracking client device interactions are provided. The system can assign an account identifier to a first segment for a pre-conversion model, responsive to receiving a first interaction associated with a content provider from a client device. The system can assign the account identifier to a second segment for the pre-conversion model, responsive to receiving a second interaction. The system can assign, the account identifier to a third segment, responsive to receiving a third interaction. The third interaction can include a conversion event. The system can generate a post-conversion model based on the third segment and the pre-conversion model. The system can determine an intent index for the account identifier based on the post-conversion model. The system can store the account identifier into an interest cluster based on the intent index.

The present invention relates to methods and systems for targeting and retargeting individuals with advertisement spots during television broadcasting. The method and system enable an advertiser for identifying and categorizing a set of viewers or individuals for retargeting advertisement based on parameters such as, but not limited to, interests or preferences of the individuals, past purchases and interactions of the individuals with the advertiser. The method and system further enable the advertiser to segregate the plurality of individuals into subgroups on the basis of information such as, but not limited to, demography, psychographic and behavioral characteristics of the plurality of individuals. The method and system then enable the advertiser to define one or more advertisement spots and corresponding advertisements to be delivered to different sub groups of individuals based on the categorization. Thereafter, the method and system retarget individuals by sending individualized messages in the one or more advertisement spots.

An electronic device is provided. The electronic device includes a display, a communicator, and a processor configured to recognize a context in a game which is running in the electronic device, receive an advertisement content from an external server through the communicator, modify the received advertisement content based on the context, and control the display to display a game screen in which the modified advertisement content is inserted.

Aspects of the subject disclosure may include, for example, providing to a user device a video content item including at least one scene which includes a plurality of advertisement placement opportunities and determining a preference profile for an individual associated with the user device. Aspects further include selecting a group of matching advertisements having advertisement profiles that match the preference profile for the individual and determining a relative prominence score for each advertisement placement opportunity. Aspects further include ordering the matching advertisements according to prominence information specified for each matching advertisement, wherein the prominence information corresponds to a relative desired prominence specified by an advertiser associated with the matching advertisement. Aspects further include providing the ordered matching advertisements to the user device according to the respective prominence information so that a matching advertisement having a greatest desired prominence is displayed in the video content item at an advertisement placement opportunity having a highest relative prominence score. Other embodiments are disclosed.