Predictive Model Generator for Short Message KPI Optimization
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Solution Overview
Problem
Marketers face challenges in authoring short messages, such as email subject lines, to achieve high key performance indicators (KPIs) like open rate and conversion rate, as existing techniques fail to provide pre-emptive insights and post-priori diagnosis, especially for short messages, and do not consider specific KPIs effectively.
Innovation Solution
A method is developed to generate predictive insights for short messages by identifying key performance indicators, generating feature vectors, determining KPI contributions using past message data, and applying these contributions to predict the message's KPI value, utilizing machine learning techniques and clustering messages based on filters like audience segments and industry verticals to create tailored predictive models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tracking is performed to determine past message performance, then the marketer can identify which message is better, but the technique fails to indicate why one message is better than another and does not provide pre-emptive insights
Solution Approach 1:
The patent introduces feature vectors as intermediary representations that bridge the gap between raw message data and KPI outcomes. These feature vectors capture semantic and structural characteristics of messages, enabling the system to explain performance differences through interpretable features rather than just black-box predictions
Solution Approach 2:
The system implements feedback loops where past message performance data is continuously fed back to refine feature vector generation and predictive models. This allows the system to learn from historical data and provide progressively better pre-emptive insights while maintaining explainability through the feature-based approach
2Measurement precision
If general message tracking is used, then overall performance can be measured, but the technique does not consider specific KPIs that the marketer considers important
Solution Approach 1:
The patent implements dynamic adaptability by allowing marketers to configure and prioritize different KPIs based on campaign objectives. The feature vector generation and predictive modeling adjust dynamically to reflect the specific KPI mix and weights chosen by the marketer, enabling versatile customization while maintaining measurement precision
Solution Approach 2:
The system enables parameter changes by allowing marketers to modify KPI definitions, weights, and priorities without restructuring the entire system. The feature vectors and predictive models adapt to these parameter changes, providing both overall performance measurement and KPI-specific insights
3Productivity
If short messages like subject lines are analyzed, then marketing communication efficiency is improved, but it becomes highly challenging to determine pre-emptive insights and provide post-priori diagnosis
Solution Approach 1:
The patent extracts key features from short messages using pre-trained language models and feature vector generation techniques. This extraction process isolates the most relevant characteristics of short messages, reducing the complexity of analysis while maintaining the ability to provide pre-emptive insights and post-priori diagnosis
Solution Approach 2:
The system replaces complex manual analysis of short messages with automated machine learning models that use feature vectors and predictive algorithms. This substitution reduces the apparent complexity for marketers while maintaining high productivity in generating actionable insights
Data Source
AI summary
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.