Computer-implemented parcel advertising methods, systems, and computer-readable media are described.

A wagering platform which matches data provided by an advertiser to situational data at a live event to deliver ads to a bettor by receiving data from an advertiser including advertisement content, conditions under which the advertisement should be delivered, and a schedule of live events during which the advertisement should be delivered to a bettor and saving the data to an advertisement database. The data in the advertisement database is compared against data from a live event, sensor and interaction data collected by a user device used by a bettor, and demographics and behavioral data relevant to the bettor can be used to identify an advertisement matching the present conditions, and further deliver the identified advertisement to the bettor.

Novel system, methods, which include machine learning, and device for providing color and fashion recommendations, including for persons with visual impairment such as color blindness or complete blindness. Also, methods providing a data storage system for storing digital renditions of garments; providing a portable communication device to extract color and/or pattern from garments through use of a camera and at least one algorithm; providing a processor capable of accessing locally stored and/or remote information about or learning the preferred matching set of garments; assigning each garment in the set of garments a red-green-blue (RGB) value; providing a suitability ranking for matching compatibility of the garment or the set of garments; and providing recommendations for preferred matching garment or set of garments by organizing the garments in at least one queue selected from the group consisting of audial, tactile, visual or a combination thereof, wherein the individual imports garments or set of garments, through a series of photos or video, for bulk import into a virtual closet for the identification and assignment of type of garments or set of garments using human or computational methods.

Aspects of the subject disclosure may include, for example, receiving an indication of a selection of a first advertisement included within a first content item from a communication device, transmitting terms of purchase associated with the first advertisement responsive to the receiving of the indication of the selection of the first advertisement, selecting a second content item based on an indication of the first content item and/or a context associated with the communication device, transmitting the second content item to the communication device responsive to the selecting of the second content item, obtaining an indication of a transaction completed for a purchase from the communication device, selecting a second advertisement responsive to the indication of the transaction, and transmitting the second advertisement to the communication device. Other embodiments are disclosed.

Systems and methods for real-time matching of promotional content to content that a user is currently consuming. Content that is currently being consumed is classified into descriptive categories, such as by determining a vector of content features where this vector is in turn used to classify the currently-played content. Promotional content having classifications that match the classifications of the currently-played content is then determined. Matching promotional content may then be played for the user in real time. In this manner, systems and processes of embodiments of the disclosure may identify promotional content matching what the user is currently watching, so as to present users promotional content tailored to subject matter the user is currently interested in.

In an embodiment, the disclosed technologies include determining a digital identifier, computing, using aggregate digital event data obtained from at least one computing device, digital feature data relating to the digital identifier, inputting the digital feature data relating to the digital identifier into a digital model that has machine-learned correlations between digital feature data and digital propensity prediction values, receiving, from the digital model, a predicted propensity value associated with the digital identifier, determining a propensity score based on the predicted propensity value, causing a digital content item to be displayed on a user interface of a network-based software application associated with the digital identifier if the propensity score satisfies a propensity criterion.

Methods and systems for generating a messaging campaign are described. A set of intended recipients is identified for a proposed messaging campaign associated with an online store. A set of recommended parameters is determined for the proposed messaging campaign, the set of recommended parameters including a recommended distribution channel for each recipient group identified in the set of intended recipients.

Methods, computer-readable media, and devices for auditing digital content to validate that the digital content is authentic, secure, and reaching the intended audience are disclosed. In one example, a method performed by a processing system including at least one processor includes launching a web browser application, wherein the launching includes instantiating a simulated user profile, and wherein the simulated user profile includes a simulated web browsing history, detecting, by the processing system, an item of digital content that is presented to the web browser application in response to the simulated user profile, determining, by the processing system, a relevance of the item of digital content to the simulated user profile, and generating, by the processing system, a report that indicates the relevance of the item of digital content to the user profile.

Techniques for using a machine-learned model to personalize content item density. In one technique, an entity that is associated with a content request is identified. Multiple sets of content items are identified that includes content items of different types. A first position of a first slot is determined in a content item feed that comprises multiple slots. A second position of a previous content item is determined, in the content item feed, that is of a first type. A difference between the first position and the second position is determined. Based on the difference, a gap sensitivity value that is associated with the entity and is different than the difference is determined. Based on the gap sensitivity value, a content item from the multiple sets of content items is selected and inserted into the first slot. The content item feed is transmitted to a computing device to be presented thereon.

Systems and methods are for providing targeted advertisements to customers at a retail store include digital display devices and digital cameras that capture physical characteristics data and behavioral data associated with the in-store customers of the retail store. The system also includes a computing device that obtains, from an electronic advertisement database, a request for the purchase of the targeted advertisements by a client wishing to digitally advertise at the store, and the physical characteristics data, behavioral data, and/or historical sales data. The computing device then correlates the request for the purchase of the targeted advertisements by the client to the physical characteristics data, behavioral data, and/or historical sales data in order to determine a schedule for displaying the targeted advertisements to in-store customers via the digital display devices, and to select one or more of the digital display devices for displaying the targeted advertisements to the in-store customers.