Adobe, Inc.

Wuyang Dai

Zhenyu Yan

Jun He

Abhishek Pani

Lei Zhang

Yi-Hong Kuo

Xinyue Liu

Polina Bartik

Chang Liu

Xiang Wu

Jalaj Bhandari

Tingting Xu

Lijun Yu

Hsiang-Yu Yang

Shiyuan Gu

Shengyun Peng

Suofei Wu

An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

Actionable KPI-driven segmentation

Methods and systems are provided for improved electronic communication campaign technologies, which can automatically balance objectives or goals of an electronic communication campaign against an overall opt-out rate for the electronic communication campaign. An electronic communications frequency optimizer can generate individual contact frequencies for individual email recipients. Embodiments can avoid unnecessary or counterproductive communications while achieving overall campaign goals, and can use processes to improve the efficiency of systems. In some cases, embodiments cluster communication recipients into different groups based on their past actions, then optimizes the communication contact frequency on different groups, to avoid performing optimization directly on millions of recipients. Some embodiments automatically self-update, for example with recipients' recent responses, to generate and/or implement campaign communication schedules on an individual level.

Multi-objective electronic communication frequency optimization

Methods and systems are provided for facilitating time zone prediction using electronic communication data. Electronic message data associated with a message recipient of electronic communications is obtained. The electronic message data includes message delivery data associated with an electronic message and message response data associated with a response, by the message recipient, to a received electronic message. Using a machine learning model and based on the message delivery data and the message response data, a time-zone score is determined for a time zone. Such a time-zone score can indicate a probability the time zone corresponds with the message recipient. Based on the time-zone score, the time zone is identified as corresponding with the message recipient.

Facilitating time zone prediction based on electronic communication data

Systems and methods for customer journey optimization in email marketing are described. The systems and methods may identify a plurality of messages for a first time period, wherein the plurality of messages are categorized according to a plurality of messages types, identify user information for a customer, wherein the user information includes user interaction data, determine a message type from the plurality of message types for the first time period based on the user information, wherein the message type is determined using a decision making model comprising a deep Q-learning neural network, select a message from the plurality of messages based on the determined message type, and transmit the message to the customer during the first time period based on the selection.

Multi-objective customer journey optimization

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

Utilizing a bayesian approach and multi-armed bandit algorithms to improve distribution timing of electronic communications

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a target distribution schedule for providing electronic communications based on predicted behavior rates by utilizing a genetic algorithm and one or more objective functions. For example, the disclosed systems can generate predicted behavior rates by training and utilizing one or more behavior prediction models. Based on the predicted behavior rates, the disclosed systems can further utilize a genetic algorithm to apply objective functions to generate one or more candidate distribution schedules. In accordance with the genetic algorithm, the disclosed systems can select a target distribution schedule for a particular user/client device. The disclosed systems can thus provide one or more electronic communications to individual users based on respective target distribution schedules.

Utilizing a genetic algorithm in applying objective functions to determine distribution times for electronic communications

Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply 'marketing platform') that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

Optimization of send time of messages

The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

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San Jose, CA, United States of America

Wuyang Dai