Causal Inference Framework for Message Send Time Optimization
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Solution Overview
Problem
Current digital marketing approaches struggle to identify the optimal time to send messages due to their inability to account for hidden confounders, leading to biased predictions and suboptimal response rates.
Innovation Solution
A causal inference framework is introduced that uses machine learning models to consider send time, recipient features, and hidden confounders, producing a ranked series of send times that maximize response rates by marginalizing the effect of confounders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital marketing approaches are used to determine send time, then the process is simple and fast, but the predictions are biased and response rates are suboptimal due to inability to account for hidden confounders
Solution Approach 1:
The patent introduces a causal inference framework as an intermediary layer between traditional marketing analytics and send time determination. This framework uses propensity score matching and causal diagrams to mediate the relationship between observed variables and hidden confounders, enabling unbiased estimation of send time effects while maintaining operational feasibility
Solution Approach 2:
The patent replaces traditional mechanical/statistical correlation-based methods with a causal inference mechanism. By substituting simple regression or A/B testing with causal diagrams and propensity score matching, the system achieves more accurate predictions of response rates while accounting for hidden confounders that traditional methods cannot detect
2Productivity
If messages are sent at arbitrary times to maximize coverage, then the reach is broad, but the response rate decreases due to inappropriate timing
Solution Approach 1:
The patent systematically varies the send time parameter across different time periods and uses causal inference to estimate the effect of each time point on response rates. By treating send time as a causal variable rather than a fixed parameter, the system identifies optimal timing that maximizes productivity while accounting for temporal confounders
Solution Approach 2:
The patent implements a feedback loop where response rate data from previous campaigns is fed into the causal inference model to refine estimates of optimal send times. This iterative feedback mechanism allows the system to continuously improve timing decisions based on actual observed outcomes, balancing reach with response rate optimization
Data Source
AI summary
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.