Zeta Global Corp.

Other

Christian Monberg

Barney Govan

Wynn Vonnegut

Gordon Michael Green

Neeraj Gore

Stefan Beeman

Tim Hudson

Dynamic content can be delivered via email. Email messages include HTML content with one or more hyperlinks referring to a network-connected dynamic content server. The hyperlinks are accessed upon rendering of the email on a client device, triggering a request to a dynamic content server. The dynamic content server determines a content item to be served in response to the request. The dynamic content item determination may be based on factors including the identity of the requester, the email communication in which the hyperlink is embedded, recent activities or behavior on the part of the requester, and/or known preference or demographic information associated with the requester. The request can be directed to a selected content item stored within a content delivery network, for transmission back to the requester and display to the user within the email.

Dynamic content delivery

Dynamic content delivery via email

A computer-implemented method for generating content recommendations for content items, each content item associated with one of a plurality of customers, the method comprising: receiving, by a network-connected server, a content request from a requesting user, the content request comprising a user identifier and a customer identifier; retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of a plurality of models and parameters for the models; retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier; routing the content request, request parameters and user data to a plurality of ranking and optimization component, each component generating a recommendation score for each recommendable resource; generating content recommendations based on the recommendation score which content recommendations are returned to the requesting user and stored by the server as a recommendation event within a repository of recommendation events; evaluating the relative efficacy of the one or more components, which models are returned to the requesting user and stored by the server as a recommendation event within a repository of recommendation events; evaluating the relative efficacy of the one or more components through continued usage; optimizing model efficacy by updating parameters within the parameter service based on the relative efficacy.

Method and apparatus for real-time personalization

Processes and apparatuses for content personalization are provided, providing for rule configuration. Content personalization systems interpret user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. Rules include filters applied to the predictive model output and/or the overall system output. Filters can be prioritized and iteratively applied or removed to adjust system output to satisfy desired result set criteria.

Personalized content system

A computer-implemented, network-connected content recommender generating content recommendations for a plurality of content servers hosted by one or more customers, the content recommender comprising: one or more processors; a memory storing instruction that, when executed by the one or more processors, cause the recommender to perform operations comprising: receiving a plurality of content recommendation requests from a querying one of said customer content servers via a plurality of input streams, each input stream including a data repository; outputting data, from the memory, associated with the content recommendation requests; receiving some or all of the data associated with said content recommendation requests; generating a first model-specific recommendation result from a first set of the plurality of received data; generating a second model-specific recommendation result from a second set of the plurality of received data; combining the first model-specific recommendation results with the second model-specific results to generate an ensemble recommendation result; and transmitting the ensemble result from the content recommender to said querying customer content server.

A computer-implemented method for sending email messages with dynamically-determined content, the method comprising: transmitting email messages to a plurality of recipient groups each comprising at least one recipients, each message comprising one or more dynamic content network references associated with dynamically-selected content items; receiving, by a network-connected server, a dynamic content network request responsive to the rendering of one of said dynamic content network references within an email message received by a recipient device, the dynamic content network request comprising one or more content selection criteria, the content selection criteria comprising an embedded user identifier uniquely associated with a user to whom an email among the email messages was transmitted; dynamically selecting a content item from amongst a plurality of candidate content items based at least in part on the content selection criteria and the recipient's recipient group; implementing an exclusion service by cross-referencing a dynamic content item with static content associated with the embedded user identifier and, based on a determination that the dynamic content item is duplicative of an associated static content item within a same email communication, serving an alternative non-duplicative content item in response to the dynamic content request; returning the selected or the alternative content item to the email recipient device for rendering within the same email communication; and determining relative effectiveness of content by recipient group.

A personalization platform is provided, which interprets user behavior and attributes along with the content users are interacting with, to build optimized predictive models of what content the user may want to see next. Those predictive models can be utilized to personalize content in one or more environments, including email, mobile and applications. An extensible and highly flexible framework can be implemented. In some embodiments, when calculating for a single user, recent behavior is scored against an ensemble of analytic models and the totals are amalgamated into a final recommendation. Any combination of analytic models may be explored and tested in a multivariate framework on this final ranking function. Models may be added and removed on a per-application basis.

In some examples, a computer-implemented method for generating content recommendations for content items is provided. Each content item is associated with one of a plurality of customers. An example method may include receiving a content request from a requesting user, the content request comprising a user identifier and a customer identifier, and retrieving request parameters from a computer-implemented parameter service, the request parameters comprising indicia of one or more recommendation strategies, and parameters for the recommendation strategies. The method may also include retrieving user data comprising a set of indicia of recommendable resources associated with the customer identifier, and routing the content request, request parameters and user data to a plurality of computer-implemented scorers, each scorer generating a recommendation score for each recommendable resource.

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San Francisco, CA, United States of America

Christian Monberg