Methods and apparatus for implementing a promotion offer language architecture for representing offer. The promotion offer language architecture includes at least an offer type field and an offer expression field. The offer type field pertains to the type of offer. The offer expression field pertains to identification of items that the offer applies to and/or the condition required to receive the benefits of the offer type. The item is identified by a quantity and a product identification.

Methods and apparatus for improving the result of online promotion testing are disclosed. In one or more embodiments, base-lining is employed to improve the mapping between the online test promotion responses and projected in-store result. Alternatively or additionally, the test population responses may be adjusted to account for differences between the test population and the in store customer population.

Method and survey server for generating an indicator of predicted survey participation data. Survey participation data are collected from a plurality of user devices. The survey participation data correspond to survey information received from users of the user devices in relation to visiting a specific website. Online profile data are collected for the plurality of user devices. The online profile data comprise at least one attribute representative of an online activity of the users of the user devices. The survey participation data and the online profile data are analyzed to generate predictive survey participation patterns for the specific website. An indicator of predicted survey participation data is generated for a current user device in relation to the specific website, based on current online profile data received for the current user device and the predictive survey participation patterns. The indicator is used by an advertisement server for advertisement selection.

Systems and methods are provided for mobile campaign optimization without knowing user identity. The system includes circuitry configured to obtain mobile application data about a mobile application from at least one mobile device. The system includes circuitry configured to generate a mobile application profile for the mobile application using the mobile application data. The system further includes circuitry configured to select at least one mobile application to show a mobile advertisement in the at least one mobile application at least partially using the mobile application profile.

Aspects of the subject disclosure may include, for example, receiving environmental data from a plurality of environmental data sources, identifying environmental risks based on the environmental data, obtaining profile information relating to a user or entity, based on the obtaining the profile information and the identifying the environmental risks, generating a recommendation for one or more locations of possible interest, and causing the recommendation to be presented to the user or entity, thereby providing location-based environmental risk information that is customized on a per user or per entity basis. Other embodiments are disclosed.

Disclosed here is a system that can obtain attributes of an advertisement, where an attribute has a continuous value, and a range of acceptable values is uncertain. The system can create a file including contents that when provided to a predetermined function produce a value of the attribute. Based on the file, the system can generate values corresponding to the attributes. Based on the generated values, the system can create the advertisement. The system can obtain a response data to the created advertisement and can fit a multidimensional function to the attributes and the user response data. Based on the multidimensional function, the system can determine next values and next ranges, where the next values and the next ranges indicate an improvement in the response data.

A ranking computer model is trained based on grouping a collection of users of an online system into different buckets based on intended likelihoods of presenting a set of content items to the collection of users, wherein a contextual bandit model is employed to compute the intended likelihoods. The online system applies the ranking computer model to generate, based on user data for a user of the online system and contextual data associated with a current session of the user, a ranking score for each content item in a set of content items. The online system selects, based on the ranking score for each content item, one or more content items from the set of content items. The online system causes a device associated with the user to display a user interface with the one or more content items for recommendation to the user.

Aspects of the subject disclosure may include, for example, receiving environmental data from a plurality of environmental data sources, identifying environmental risks based on the environmental data, obtaining profile information relating to a user or entity, based on the obtaining the profile information and the identifying the environmental risks, generating a recommendation for one or more locations of possible interest, and causing the recommendation to be presented to the user or entity, thereby providing location-based environmental risk information that is customized on a per user or per entity basis. Other embodiments are disclosed.

Systems and methods for optimizing the combination of products and services a business offers to customers, identifying a combination of top markets a business offers to customers for growth opportunities, and optimizing advertisements. In one implementation, the disclosed system includes at least one processing device and at least one non-transitory memory containing software code configured to cause the processing device to: gather customer data and financial institution data from a plurality of data sources; extract a plurality of customer behavior features and a plurality of financial institution behavior features; process the customer behavior features and financial institution behavior features using one or more trained foundation models; input the foundation model outputs and a plurality of goal inputs into a trained product model; and output a natural-language advertisement response.

Methods, systems, and media for determining the impact of content campaigns using surveys are provided. In some embodiments, approaches for measuring the impact of content campaigns with attitudinal metrics assessed through surveys are provided.