Abstract: Disclosed is a computer implemented method of creating a profile of a user based on user behavior. The method may include receiving a plurality of Universal Resource Locators (URLs) corresponding to a plurality of webpages visited by the user. Further, the method may include retrieving content from each of the plurality of webpages based on the plurality of URLs. Furthermore, the method may include analyzing content from each of the plurality of webpages. Additionally, analyzing content from a webpage may include analyzing content corresponding to each content type present on the webpage. Further, the method may include identifying a plurality of keywords corresponding to the webpage based on the analyzing. Furthermore, the plurality of keywords may be associated with a plurality of affinity values. The plurality of keywords and the plurality of affinity values may constitute the profile of the user.

Abstract: Disclosed is a computer implemented method of providing business intelligence based on user behavior. The method may include a step of receiving a user identifier associated with the user from a requesting entity, such as a server computer. Further, the method may include a step of identifying an anonymous identifier corresponding to the user identifier. Additionally, the method may include a step of retrieving anonymous user behavior data based on the anonymous identifier. Furthermore, the method may include a step of transmitting the anonymous user behavior data to the requesting entity. Accordingly, the anonymous user behavior data may be used by the requesting entity to, for example, to enrich data, such as CRM data, of the user with the anonymous user behavior data.

Abstract: Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Abstract: Disclosed is an online platform for predicting consumer interest level based on online behavior of consumers. The platform may be configured to monitor online activity of consumers across a plurality of webpages. As a result, the plurality of webpages visited by the consumer may be determined by the online platform. Further, the online platform may be configured to access and parse each webpage in the plurality of webpages in order to extract key elements. Furthermore, the platform may be configured to aggregate key elements extracted from each of the plurality of webpages and perform an analysis. Based on the analysis, the consumer may be determined to be in-market with regard to a product and/or a service. Further, based on the analysis, a confidence value representing a degree to which the consumer is in-market with respect to the product and/or the service may also be determined.

Abstract: Disclosed is a computer implemented method of creating an audience list based on user behavior data. The method may include receiving, by a processor, filtering criteria for identifying the audience list. Further, the method may include filtering, by the processor, set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data. Additionally, the method may include identifying, by the processor, a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.

Abstract: Disclosed is a computer implemented method of creating a profile of a user based on user behavior. The method may include receiving a plurality of Universal Resource Locators (URLs) corresponding to a plurality of webpages visited by the user. Further, the method may include retrieving content from each of the plurality of webpages based on the plurality of URLs. Furthermore, the method may include analyzing content from each of the plurality of webpages. Additionally, analyzing content from a webpage may include analyzing content corresponding to each content type present on the webpage. Further, the method may include identifying a plurality of keywords corresponding to the webpage based on the analyzing. Furthermore, the plurality of keywords may be associated with a plurality of affinity values. The plurality of keywords and the plurality of affinity values may constitute the profile of the user.

Abstract: Disclosed is a computer implemented method of creating a profile of a user based on user behavior. The method may include receiving a plurality of Universal Resource Locators (URLs) corresponding to a plurality of webpages visited by the user. Further, the method may include retrieving content from each of the plurality of webpages based on the plurality of URLs. Furthermore, the method may include analyzing content from each of the plurality of webpages. Additionally, analyzing content from a webpage may include analyzing content corresponding to each content type present on the webpage. Further, the method may include identifying a plurality of keywords corresponding to the webpage based on the analyzing. Furthermore, the plurality of keywords may be associated with a plurality of affinity values. The plurality of keywords and the plurality of affinity values may constitute the profile of the user.

Abstract: Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Abstract: Disclosed is a method and system for providing map-based visualization of user interaction data. The platform may be configured to monitor online activity of consumers across a plurality of webpages. As a result, the plurality of webpages visited by the consumer may be determined by the online platform. Further, the online platform may be configured to access and parse each webpage in the plurality of webpages in order to extract key elements. Furthermore, the platform may be configured to aggregate key elements extracted from each of the plurality of webpages and perform an analysis. Based on the analysis, the consumer may be determined to be in-market with regard to a product and/or a service. Further, based on the analysis, providing a location-based user interface for viewing online behavior data associated with individuals located in different places may also be determined.

Abstract: Disclosed is an online platform for predicting consumer interest level based on online behavior of consumers. The platform may be configured to monitor online activity of consumers across a plurality of webpages. As a result, the plurality of webpages visited by the consumer may be determined by the online platform. Further, the online platform may be configured to access and parse each webpage in the plurality of webpages in order to extract key elements. Furthermore, the platform may be configured to aggregate key elements extracted from each of the plurality of webpages and perform an analysis. Based on the analysis, the consumer may be determined to be in-market with regard to a product and/or a service. Further, based on the analysis, a confidence value representing a degree to which the consumer is in-market with respect to the product and/or the service may also be determined.

Abstract: Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

Abstract: Disclosed is a computer implemented method of providing business intelligence based on user behavior. The method may include a step of receiving a user identifier associated with the user from a requesting entity, such as a server computer. Further, the method may include a step of identifying an anonymous identifier corresponding to the user identifier. Additionally, the method may include a step of retrieving anonymous user behavior data based on the anonymous identifier. Furthermore, the method may include a step of transmitting the anonymous user behavior data to the requesting entity. Accordingly, the anonymous user behavior data may be used by the requesting entity to, for example, to enrich data, such as CRM data, of the user with the anonymous user behavior data.

Abstract: Disclosed is a computer implemented method of creating an audience list based on user behavior data. The method may include receiving, by a processor, filtering criteria for identifying the audience list. Further, the method may include filtering, by the processor, set of users based on the filtering criteria, wherein each user in the set of users is associated with user behavior data. Additionally, the method may include identifying, by the processor, a plurality of users from the set of users based on the filtering, wherein the plurality of users constitutes the audience list.

Abstract: Disclosed is a computer implemented method of creating a profile of a user based on user behavior. The method may include receiving a plurality of Universal Resource Locators (URLs) corresponding to a plurality of webpages visited by the user. Further, the method may include retrieving content from each of the plurality of webpages based on the plurality of URLs. Furthermore, the method may include analyzing content from each of the plurality of webpages. Additionally, analyzing content from a webpage may include analyzing content corresponding to each content type present on the webpage. Further, the method may include identifying a plurality of keywords corresponding to the webpage based on the analyzing. Furthermore, the plurality of keywords may be associated with a plurality of affinity values. The plurality of keywords and the plurality of affinity values may constitute the profile of the user.