You've got e-mail!

You've got e-mail!

Problem being addressed

Email communications are ubiquitous and firms control send times of emails and thereby the instants at which emails reach recipients; however​,​ they do not control the duration it takes for recipients to open emails​,​ labeled as time-to-open.

Solution

Emails are likely to be opened sooner when send times are convenient for recipients, while for other send times, emails can get ignored. Thus, to compute appropriate send times it is important to predict times-to-open accurately. The researchers propose a framework to predict times-to-open, for each recipient. Using that they compute appropriate send times. The sequence of emails received by a person from a sender is a result of interactions with past emails from the sender, and hence contains useful signal that inform the model. The best times to send emails can be computed accurately from predicted times-to-open. This approach allows a firm to tune send times of emails, which is in its control, to favorably influence open rates and engagement.

Advantages of this solution

In predicting times-to-open, the suggested model outperforms other methods which ignore the sequential time dependence in email interactions. Moreover, the proposed approach can be successfully used for computing appropriate send times.

Solution originally applied in these industries

advertising

Advertising Industry

Possible New Application of the Work

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Entertainment Industry

This approach can be used in predicting listening behaviour on music websites in order to better target the audience for the new releases.

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Marketing

The approach can be successfully used in marketing to predict customer life-time value and customer churn.

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

Media

The suggested model is efficient in estimating reciprocal-link creation time in online social networks.

Author of original research described in this blitzcard:

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Name of the author who conducted the original research that this blitzcard is based on.

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