Abstract: A method and system of managing a new client record for a client record management system, including creating a new client record, comparing the new client record against at least an existing client record in a database, generating a table with a picklist, with the picklist presenting a suggested record, which is selected from an existing client record in the database, displaying the table and presenting the picklist for a confirmation by a user, and merging the new client record with the existing client record based on the confirmation by the user, where the picklist presents the suggested record based on a criterion, the criterion being a match in a field between the new client record and the suggested record.

Abstract: A method and system of managing a client record for a client record management system, including accessing a database of client records, retrieving a list of non-compliant client records and displaying this, filtered by a category where a non-compliant client record can be selected for a template email to be sent, and receiving a reply that includes a consent, with a subsequent communication is sent to the email address.

Abstract: A method and system of managing a new client record for a client record management system, including creating a new client record, comparing the new client record against at least an existing client record in a database, generating a table with a picklist, with the picklist presenting a suggested record, which is selected from an existing client record in the database, displaying the table and presenting the picklist for a confirmation by a user, and merging the new client record with the existing client record based on the confirmation by the user, where the picklist presents the suggested record based on a criterion, the criterion being a match in a field between the new client record and the suggested record.

Abstract: Systems and methods are provided that presents interesting local content to users at times when the content is most actionable and engaging without the user explicitly entering a search term or expressing intent. In one implementation, the system uses contextual signals about a user to rank local content. Signals may include, for example, who they (one or more users) are, what kinds of places they like, where they are, how familiar they are with the area, the time of day, where the user's friends have been nearby, among other information related to the one or more users. A system may be provided that uses contextual rules and machine learning to target content to users. The system learns which of these contextual signals are most important and alters its ranking function to optimize user engagement in terms of conversions.