Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.