Content Optimization System Using Iterative Weighting
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Solution Overview
Problem
Current online marketing optimization methods, such as A/B testing and multivariate testing, are labor-intensive and inefficient in determining optimal content parameters for improving recipient response rates, especially when dealing with complex content items and varying touch-points, as they require extensive data processing and are limited in interaction effect analysis.
Innovation Solution
A computer-implemented method and system that generates content samples based on combination weightings derived from recipient response data, dynamically adapting testing by focusing on combinations likely to result in high response rates, using a data structure like a tree structure to store and analyze recipient responses, and iteratively improving content samples based on statistical criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive testing of all combinations of content parameters is performed one-by-one, then the accuracy of results is improved, but the time required and number of samples needed increases considerably
Solution Approach 1:
The patent segments the exhaustive testing process into iterative batches. Instead of testing all combinations sequentially, it divides content parameter combinations into multiple testing batches, where each batch tests a subset of combinations. This allows parallel processing and reduces the time required while maintaining result accuracy through multiple iterations.
Solution Approach 2:
The patent performs preliminary actions by generating initial content parameter combinations and testing them in early batches. Based on results from previous batches, it pre-calculates and prioritizes which combinations to test next, focusing computational resources on the most promising combinations before exhaustive testing is complete.
2Loss of information
If combinations of several content parameters are varied from content item to content item, then the depth of analysis is improved, but uncertainty regarding the true performance impact of each parameter increases
Solution Approach 1:
The patent implements feedback loops where results from each testing batch are analyzed and used to adjust the selection of combinations for the next batch. By incorporating feedback from previous iterations, the system can deconstruct multi-parameter variations and attribute performance impacts to individual parameters more accurately, reducing uncertainty.
Solution Approach 2:
The patent uses partial action by testing a representative subset of parameter combinations rather than all possible combinations simultaneously. Through multiple iterative batches, it gradually builds up complete knowledge of parameter impacts, achieving deep analysis while managing uncertainty through controlled experimentation.
3Adaptability or versatility
If the number of content parameters to be tested is increased, then the comprehensiveness of optimization is improved, but the trade-off between parameters to test and accuracy of results worsens
Solution Approach 1:
The patent adds the dimension of time through iterative batches. Instead of attempting to test all parameter combinations simultaneously (which would dilute accuracy), it extends the testing process across multiple time iterations, with each batch focusing on specific combinations. This transforms a spatial problem (testing many combinations at once) into a temporal problem (testing combinations sequentially over time), maintaining accuracy while achieving comprehensive optimization.
Data Source
AI summary
A computer-implemented method and system for improving recipient response rates to content presented via a data communications network to a plurality of recipients based upon recipient response data, wherein the content comprises a plurality of content factors. Each content factor is selected from one or more corresponding content levels. A data structure is provided for containing recipient response data associated with content comprising combinations of content levels. A plurality of content samples is generated by forming combinations of content levels for a plurality of content factors in accordance with respective combination weightings derived from recipient response data contained within the data structure. The plurality of content samples is presented to a corresponding plurality of recipients via the data communications network. The recipient responses to the content samples are received via the data communications network. The recipient responses are analysed to identify one or more combinations of content levels resulting in high recipient response rates relative to other combinations in accordance with predetermined statistical criteria. The recipient response data associated with combinations of content levels resulting in high recipient response rates is stored within the data structure.