The Rocket Science Group LLC

John Foreman

Cass Petrus

Benjamin Chestnut

Chad Morris

Nathaniel Bainton

Sean Sawyer

William Hoffmann

Joe Uhl

Mardav Wala

Michaela Moore

Nitish Pandey

Jason Veatch

Guan Liao

Certain embodiments involve stopping potential flagged email, such spam, before it becomes flagged email. For instance, certain embodiments can make a prediction (e.g., spam or other flagged email) about a 'proposed' email message to a list of email addresses of proposed recipients. The flagged email prediction is generated based on the list of email addresses of recipients. The flagged email prediction can involve using stored data about historic email addresses, historic lists of email addresses, historic email transactions, or some combination thereof to calculate historic hard bounce rates. The historic hard bounce rates can be used to train processes to calculate the hard bounce rates for the email addresses on the list and the list of email addresses.

Automatically predicting that a proposed electronic message is flagged based on a predicted hard bounce rate

This disclosure involves modifying messaging data having unknown attribute values associated with entities to facilitate retrieval of address data for communications with the entities. For example, a system accesses a mapping of first addresses to an attribute, wherein the first addresses include (1) a target address for a target entity and (2) addresses associated with first entities in turn associated with first known values of the attribute. The system accesses a mapping of second addresses to an attribute, wherein the second addresses include (1) the target address for the target entity and (2) addresses associated with second entities in turn associated with second known values of the attribute. The system determines distributions of the first known values and the second known values, predicts a value of the attribute for the target entity based thereon, updates the messaging data therewith, and services a query for addresses having the predicted value.

Updating messaging data structures to include predicted attribute values associated with recipient entities

In certain embodiments, evaluations of effectiveness are optimized for multiple electronic message versions. For example, a server receives, subsequent to first electronic messages being transmitted over a first time period, responsive electronic data automatically generated by interactions with these messages. The server controls, based on the responsive electronic data, transmission of second electronic messages to recipients. For instance, the server provides an interface for configuring a test transmission of different test message versions to segments of recipients, where each version includes a different combination of message attributes. The server receives a selection of options via the interface for the test transmission, identifies an adverse impact on the test transmission associated with the selection, and indicates the adverse impact via the interface. The server subsequently receives, via the interface, a modification to the test transmission options. The server uses the modified options to transmit the second electronic messages.

Optimizing evaluation of effectiveness for multiple versions of electronic messages

Methods and systems for predicting a proposed electronic mail (email) for an email campaign to be sent to a list of email addresses received from a sender is spam. A hard bounce rate is predicted for the list of email addresses based on hard bounce rates calculated for respective email addresses on the list. If predicted hard bounce rate exceeds a threshold, the proposed email is deemed spam and sender may be informed. If predicted hard bounce rate falls within range of the threshold, proposed email may be sent to less than all the email addresses on list. If an acceptable number of the sent emails are received, the email may be sent to remaining email addresses on list. Prior to sending proposed email, the list of email addresses may be compared to purchased lists of email addresses. If there is a match, the proposed email may be deemed spam.

Methods and systems for predicting a proposed electronic message as spam based on a predicted hard bounce rate for a list of email addresses

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Avondale Estates, GA, United States of America

John Foreman