Message Campaign Grammar Recommendation via Historical Data Ranking
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Solution Overview
Problem
Existing message optimization systems rely on manual selection of starting values by campaign managers, which can be ineffective for inexperienced users, as they lack knowledge of optimal initial values for message campaigns.
Innovation Solution
A system and method that recommend a grammar structure for message campaigns based on statistical design budgets, identifying relevant past campaigns, and ranking message component values by performance, allowing campaign managers to select and refine values while providing alternate options.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual selection of starting values is used by campaign managers, then the system is simple to operate, but inexperienced users cannot select optimal initial values leading to poor campaign performance
Solution Approach 1:
The system automatically generates and recommends starting values for message components by analyzing historical campaign data, allowing the system to serve itself rather than relying on manual user input. This resolves the contradiction by making the system both easy to operate (automatic value generation) and reliable (data-driven recommendations).
Solution Approach 2:
The system performs preliminary analysis of historical campaign data to pre-calculate optimal starting values before the user needs them. By preparing recommendations in advance based on past performance, the system ensures both ease of operation (values are ready) and reliability (values are optimized).
2Reliability
If automated recommendation system is implemented, then optimal starting values can be provided, but system complexity increases
Solution Approach 1:
The system copies successful patterns and structures from historical campaigns that have proven effective, rather than creating entirely new optimization logic. By replicating what has worked before, the system achieves reliability without excessive complexity.
Solution Approach 2:
The system uses a universal approach by analyzing multiple historical campaigns across different contexts to generate recommendations that can apply to various new campaigns. This multi-functional analysis capability provides reliable recommendations without requiring separate complex systems for each campaign type.
3Reliability
If extensive analysis of past campaigns is performed, then better recommendations are generated, but time required for setup increases
Solution Approach 1:
The system performs preliminary analysis of historical campaign data in advance, building a knowledge base of successful patterns before new campaigns are created. This pre-computation ensures that when a user needs recommendations, they are generated quickly without requiring extensive real-time analysis.
Solution Approach 2:
The system replaces manual mechanical analysis of historical data with automated computational algorithms that can quickly process and analyze large volumes of past campaign data. This substitution of automated computation for manual or extensive mechanical analysis achieves both accuracy and speed.
Data Source
AI summary
A system and method is provided for recommending a grammar for a message campaign used by a message optimization system. A user specifies parameters for a new campaign, from which a set of statistical design budgets is calculated. The user selects a grammar structure, recommended based on the statistical design budgets, for the campaign. The n-most relevant past campaigns are identified. Semantic tags, associated with each previously used value from the n-most relevant past campaigns and each of a plurality of untested values, are identified and ranked based on past performance. The previously used values are ordered by ranked tag group and then within each tag group, while the untested values are ordered by ranked tag group and then randomly within the tag group. Recommended values are selected from the ranked list of previously used values and untested values depending on the degree of exploration/conservatism indicated by the user.