Systems and methods for delivery aware audience segmentation and subsequent delivery of content are described. Embodiments are configured to obtain activity data for a user, assign the user to a user segment based on the activity data using a machine learning model, generate a reach prediction for the user segment, select a media channel for communicating with the user based on the user segment and the reach prediction, and provide targeted content to the user via the selected media channel. According to some aspects, the machine learning model is trained based on content reach data.

In some embodiments, a method analyzes a characteristic of a segment list for a user identifier, and compares the characteristic for the segment list to two or more thresholds. A storage type is determined for the segment list based on the comparing. A value for the segment list is stored using a storage type from a plurality of storage types based on the characteristic for the segment list meeting a threshold for the storage type from the two or more storage types. The method stores the value for the segment list in storage, wherein the value is stored based on the storage type.

The present disclosure provides a computer-implemented method and system for granular level segmentation of users based on online activities on a webpage in real-time. The computer-implemented method and system corresponds to a user segmentation system. The user segmentation system receives a first set of data associated with a plurality of users. The user segmentation system fetches a second set of data. The user segmentation system obtains a third set of data. The user segmentation system analyzes the first set of data, the second set of data and the third set of data using one or more machine learning algorithms. The user segmentation system creates one or more segments based on analysis performed on the first set of data, the second set of data and the third set of data. The user segmentation system initiates one or more marketing campaigns for the one or more segments.

Systems, methods and articles of manufacture for delivering website content to an internet user which is personalized to the user based on a persona associated with the user. A persona database system accesses personal and financial data for the user from any suitable source, such as from a tax return of the user or personal finance management application or even a questionnaire. The persona database system matches the user's data to a persona for the user from a predetermined, discrete set of personas, wherein each persona identifies a generalized profile of personal and financial characteristics of the user. Then, when a user access a website hosted by a website server, the website server accesses the persona for the user from the persona database system and the website server personalizes the website content delivered to the user based on the persona for the user.

The systems and methods may be used to recommend an item to a consumer. The methods may comprise determining, based on a collaborative filtering algorithm, a consumer relevance value associated with an item, and transmitting, based on the consumer relevance value, information associated with the item to a consumer. A collaborative filtering algorithm may receive as an input a transaction history associated with the consumer, a demographic of the consumer, a consumer profile, a type of transaction account, a transaction account associated with the consumer, a period of time that the consumer has held a transaction account, a size of wallet, and/or a share of wallet. The method may further comprise generating a ranked list of items based upon consumer relevance values, transmitting a ranked list of items to a consumer, and/or re-ranking a ranked list of items based upon a merchant goal.

The disclosure relates to a computer server system implementing a method to obtain a plurality of online articles for display on a webpage; obtain a candidate promoted content for each of the plurality of online articles; for each of the plurality of online article and the corresponding candidate promoted content pairs: determine a virality score of the online article indicating popularity of the online article among online users; determine a similarity score indicating similarity between the online article and the candidate promoted content; determine a qualification score based on the virality score and the similarity score; select a pair of target article and target promoted content from the plurality of article and candidate promoted content pairs based on the corresponding qualification scores; and display the target promoted content on the webpage.

A computer-implemented method of using the Internet to promote goods and services and connect merchants with potential purchasers in chat groups who wish to obtain suitable sources of goods and services is provided, wherein a plurality of users each have a computer device provided with chat application software and software for accessing and interactively communicating via a computer network with a server provided with a search engine for searching the Internet. Users initiate a chat conversation among a group of users. One of the users invokes a search application using the search engine. The user conducts a search of the Internet for products or services, reviews the results of the search, selects a product or service located by the search, and shares the selected search result with the chat conversation. One of the users can order the selected product or service as part of the process.

The present disclosure is directed to associating computing devices with each other based on computer network activity for selection of content items as part of an online content item placement campaign. A first linking factor is identified based on a connection between a first device and the computer network via a first IP address during a first time period, and based on a connection between a second device and the computer network via the first IP address during the first time period. A number of devices that connect with the computer network via the first IP address is determined. A positive match probability is generated. A second and third linking factors are monitored. A negative match probability is determined based on the second and third linking factors. The first device is linked with the second device based on the positive and negative match probabilities.

The present disclosure is directed to associating computing devices with each other based on computer network activity for selection of content items as part of an online content item placement campaign. A first linking factor is identified based on a connection between a first device and the computer network via a first IP address during a first time period, and based on a connection between a second device and the computer network via the first IP address during the first time period. A number of devices that connect with the computer network via the first IP address is determined. A positive match probability is generated. A second and third linking factors are monitored. A negative match probability is determined based on the second and third linking factors. The first device is linked with the second device based on the positive and negative match probabilities.

Targeting information (also referred to as ad serving constraints) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.