Adobe, Inc.

Neolane Sas

Frederic Mary

Tanya Goyal

Kokil Jaidka

Vishwa Vinay

Harvineet Singh

Moumita Sinha

The present disclosure relates applying a survival analysis to model when a particular recipient will view an electronic message. For example, one or more embodiments train a survivor function to model the time that will elapse, on a continuous scale, before a recipient will open an electronic message. For example, one or more embodiments involve accessing analytics training data and extracting a first set of features affecting the time that elapsed before past recipients opened an electronic message and a second set of features affecting whether the recipients opened the electronic message at all. The system then generates a mixture model modified survivor function and determines the effect of each feature set on its corresponding outcome to learn parameters for the mixture model modified survivor function.

Modeling time to open of electronic communications

A method for generating predictive insights for authoring messages is provided. The method includes receiving a message to be sent as an input. Key performance indicator (KPI) whose value is to be predicted for the message is identified from the input or marketing tool configuration. A plurality of feature vectors of the message are generated. KPI contributions for the plurality of feature vectors are determined using feature vectors of messages sent in past and tracked KPI values of the messages sent in past. The KPI contribution is a measure of contribution of feature vector to value of the KPI. Value of the KPI for the message is predicted by applying determined KPI contributions to the plurality of feature vectors. Apparatus for substantially performing the method as described herein is also provided.

Generating predictive models for authoring short messages

Method and apparatus for generating predictive insights for authoring short messages

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Maisons-Alfort, France

Frederic Mary