Machine Learning Engagement Prediction Using List Vocabulary Vectors
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Solution Overview
Problem
Current machine learning systems face challenges in predicting engagement rates for target recipient lists without accompanying demographic, psychographic, or behavioral information, which is typically required for effective marketing campaign analysis.
Innovation Solution
The system constructs a vocabulary from electronic lists, generates feature vectors for each list, and trains a machine learning model using these vectors, allowing for the prediction of engagement rates for target lists by comparing their similarity to training corpus lists, even without additional information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional machine learning systems use only electronic lists without demographic, psychographic, or behavioral information, then the system can operate with limited data availability, but the prediction accuracy of engagement rates deteriorates
Solution Approach 1:
The patent segments the feature extraction process into distinct components: vocabulary construction from electronic lists, feature vector generation based on list characteristics, and similarity comparison with training corpus. This segmentation allows the system to work effectively with limited data by focusing on extracting meaningful features from the available electronic lists alone, rather than requiring complete demographic and behavioral data for each recipient.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation between the raw electronic lists and the machine learning model. These feature vectors capture essential characteristics of the recipient lists (such as list size, engagement metrics, and content features) and enable the model to make predictions based on similarity comparisons with the training corpus, bridging the gap between limited input data and accurate engagement rate predictions.
2Adaptability or versatility
If the system processes large volumes of electronic lists to construct vocabularies and generate feature vectors, then the model can be trained on diverse data, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary processing by constructing vocabularies and generating feature vectors for the training corpus in advance, before the actual model training and prediction phases. This preliminary action organizes the data into structured representations that can be efficiently stored and quickly compared during inference, reducing the computational burden during real-time operations while still allowing the model to learn from diverse electronic lists.
3Ease of operation
If the system uses similarity comparison with training corpus lists to predict engagement rates, then the system can provide predictions without additional recipient information, but the requirement for a comprehensive training corpus increases
Solution Approach 1:
The patent uses similarity comparison between feature vectors of target recipient lists and those in the training corpus, effectively copying and comparing structural patterns rather than requiring identical detailed information. This approach allows the system to make predictions by finding analogous cases in the training corpus, reducing the need for a vast training corpus while maintaining prediction capability through pattern recognition rather than exhaustive data matching.
Data Source
AI summary
Techniques are described for training machine learning (ML) models using one or more electronic lists of items previously used in campaigns and labeled with an engagement rate corresponding to the list. A vocabulary formed from a union of the one or more lists may then be used to generate at least some items of a target recipient list. An engagement rate for the target recipient list may be inferred for the target recipient list. Natural language processing (NLP) techniques may be also be applied to optimize an engagement rate of a target recipient list and/or select content for the list.