Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: Embodiments of the present invention provide a system for fault tolerant case creation. The system is typically configured for receiving an application specific input associated with an application from a user via a user portal, storing the application specific input in a repository, accessing the repository to determine a plurality of predefined triggering parameters, identifying occurrence of first external event, creating an instance of the first external event, adding the first external event and the associated instance in a queue as a first entry, de-queuing the first entry, invoking one or more codes for creating a first case for the first external event and the associated instance, identifying that the creation of the first case by the one or more codes is successful, and generating a report comprising information associated with the successful creation of the first case.

Abstract: Embodiments of the present invention provide a system for fault tolerant case creation.

Abstract: An system can include a server that includes or is associated with a keyword price recommendation module. The module can be configured to recommend an optimal price for a keyword, per referral event, in a search engine marketing campaign. For making such a recommendation, the module can be configured to determine revenue generated per referral event for the keyword according to a number of referral events for the keyword for a plurality of users and revenue generated for the keyword by the plurality of users. It can also be configured to select a return on investment percentage for the keyword, and determine the optimal price for the keyword according to the revenue generated per referral event and the return on investment percentage. Additionally or alternatively, the keyword price recommendation module can be configured to calculate the return on investment percentage for the keyword according to at least one market condition.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An example system can include a server that includes or is associated with a keyword recommendation module. The keyword recommendation module can be configured to select keywords for a search engine for use in a search engine marketing campaign, wherein the search engine provides more traffic to Internet content of a publisher than other search engines, for the keywords. The module can also be configured to determine, per keyword, user engagement with the Internet content resulting from the traffic provided by the search engine for the keywords, according to one or more of time spent viewing the Internet content, page views of the Internet content, and dwell times. The module can also be configured to score, per keyword, the keywords according to the determined user engagement with the Internet content, and generate keyword recommendations according to the scoring of the keywords.

Abstract: An system can include a server that includes or is associated with a keyword price recommendation module. The module can be configured to recommend an optimal price for a keyword, per referral event, in a search engine marketing campaign. For making such a recommendation, the module can be configured to determine revenue generated per referral event for the keyword according to a number of referral events for the keyword for a plurality of users and revenue generated for the keyword by the plurality of users. It can also be configured to select a return on investment percentage for the keyword, and determine the optimal price for the keyword according to the revenue generated per referral event and the return on investment percentage. Additionally or alternatively, the keyword price recommendation module can be configured to calculate the return on investment percentage for the keyword according to at least one market condition.

Abstract: A system and method automatically transforms preexisting advertising creatives adapted for a first content service environment into advertising creatives for a second content service environment. The transformed advertising creative are then suitable for initiating a new advertising campaign. In one example, the system transforms advertising creatives from search advertisements and display advertisements into creatives for a stream advertising campaign. Stream advertisements may then be placed into a stream of content displayed on a user device. The transformed advertising creatives may be included in a proposal to a prospective advertiser. The advertiser then can choose to accept the proposal. The process of creating new advertising creatives for the proposal to the advertiser is simplified, automated and made more efficient.